Abstract Hyperspectral image super-resolution (HSI-SR) aims to reconstruct hyperspectral images at high spatial resolution, starting from low-resolution inputs, while preserving both spatial details and spectral fidelity. In this work, we propose the Efficient Spatial-Spectral Processing Network (ESSPN), a lightweight deep learning architecture designed to address the challenges of HSI-SR in a computationally efficient way. ESSPN is built around a novel Spatial-Spectral Block (SSB) that separately models spatial structures and spectral correlations through residual convolutional and attention mechanisms. The network head incorporates an efficient upsampling module based on pixel shuffle decomposition to produce high-resolution outputs without interpolation artifacts. Extensive experiments on two publicly available datasets, i.e., ARAD1K and StereoMSI demonstrate that ESSPN achieves competitive or superior performance compared to state-of-the-art methods when evaluated at scale factors of $$\times $$ 4, $$\times $$ 6 and $$\times $$ 8. Notably, the model shows strong generalization across hyperspectral cameras with varying spectral responses, and across radiometric domains, covering both radiance and reflectance measurements, while requiring significantly fewer parameters and FLOPs compared to existing methods. These results position the proposed ESSPN as a practical and effective solution for high-quality hyperspectral image super-resolution in real-world applications.

Accurate color information plays a critical role in numerous computer vision tasks, with the Macbeth ColorChecker being a widely used reference target due to its colorimetrically characterized color patches. However, automating the precise extraction of color information in complex scenes remains a challenge. In this paper, we propose a novel method for the automatic detection and accurate extraction of color information from Macbeth ColorCheckers in challenging environments. Our approach involves two distinct phases: (i) a chart localization step using a deep learning model to identify the presence of the ColorChecker, and (ii) a consensus-based pose estimation and color extraction phase that ensures precise localization and description of individual color patches. We rigorously evaluate our method using the widely adopted NUS and ColorChecker datasets. Comparative results against state-of-the-art methods show that our method outperforms the best solution in the state of the art achieving about 5% improvement on the ColorChecker dataset and about 17% on the NUS dataset. Furthermore, the design of our approach enables it to handle the presence of multiple ColorCheckers in complex scenes. Code will be made available after pubblication at: https://github.com/LucaCogo/ColorChartLocalization . • A robust and precise method for Camera-Independent Color Chart Localization is proposed. • The method has two phases: a chart localization step, and a consensus-based pose estimation. • The method can handle the presence of multiple color targets in complex scenes. • The method outperforms the state-of-the-art solution by up to about 17% on standard datasets.

We present a novel Convolutional Neural Network that exploits the Laplacian decomposition technique, which is typically used in traditional image processing, to restore videos compressed with the High-Efficiency Video Coding (HEVC) algorithm. The proposed method decomposes the compressed frames into multi-scale frequency bands using the Laplacian decomposition, it restores each band using the ad-hoc designed Multi-frame Residual Laplacian Network (MRLN), and finally recomposes the restored bands to obtain the restored frames. By leveraging the multi-scale frequency representation of compressed frames provided by the Laplacian decomposition, MRLN can effectively reduce the compression artifacts and restore the image details with a reduced computational cost. In addition, our method can be easily instantiated in various versions to control the tradeoff between efficiency and effectiveness, representing a versatile solution for scenarios with constrained computational resources. Experimental results on the MFQEv2 benchmark dataset show that our method achieves the state-of-the-art performance in HEVC-compressed video restoration with a lower model complexity and shorter runtime with respect to existing methods. The project page is available at https://github.com/claudiom4sir/LaplacianVCAR .

Littering is a major problem that threatens the environment, society, and economy. Keep track, monitor and regularly clean littering sites can be a crucial problem that involves public authorities, municipalities, companies, and citizens. So far approaches have not well leveraged the knowledge and capabilities that derive from the federation of multiple communities, such as cities, public bodies, and organizations. In this paper, we describe the COBOL project, a National PRIN (Progetti di Rilevante Interesse Nazionale) PNRR (Piano Nazionale Ripresa e Resilienza) project funded by the Italian MUR (Ministero dell'Università e della Ricerca) in 2023. The project aims to definite a flexible framework for managing the waste disposal process through a federated learning architecture that collects and integrates the reports (e.g., annotated pictures and user feedback) shared by the communities involved in the waste disposal process. To deliver an advanced waste disposal service based on the direct participation of citizens, COBOL also integrates Model-Driven Engineering principles, Computer Vision techniques, and Self-Adaptation mechanisms. Early results show that reports can be effectively collected and processed with COBOL.

Performance in Speech Emotion Recognition (SER) on a single language has increased greatly in the last few years thanks to the use of deep learning techniques. However, cross-lingual SER remains a challenge in real-world applications due to two main factors: the first is the big gap among the source and the target domain distributions; the second factor is the major availability of unlabeled utterances in contrast to the labeled ones for the new language. Taking into account previous aspects, we propose a Semi-Supervised Learning (SSL) method for cross-lingual emotion recognition when only few labeled examples in the target domain (i.e. the new language) are available. Our method is based on a Transformer and it adapts to the new domain by exploiting a pseudo-labeling strategy on the unlabeled utterances. In particular, the use of a hard and soft pseudo-labels approach is investigated. We thoroughly evaluate the performance of the proposed method in a speaker-independent setup on both the source and the new language and show its robustness across five languages belonging to different linguistic strains. The experimental findings indicate that the unweighted accuracy is increased by an average of 40% compared to state-of-the-art methods.

Low-light video enhancement (LLVE) has received little attention compared to low-light image enhancement (LLIE) mainly due to the lack of paired low-/normal-light video datasets. Consequently, a common approach to LLVE is to enhance each video frame individually using LLIE methods. However, this practice introduces temporal inconsistencies in the resulting video. In this work, we propose a recurrent neural network (RNN) that, given a low-light video and its per-frame enhanced version, produces a temporally consistent video preserving the underlying frame-based enhancement. We achieve this by training our network with a combination of a new forward-backward temporal consistency loss and a content-preserving loss. At inference time, we can use our trained network to correct videos processed by any LLIE method. Experimental results show that our method achieves the best trade-off between temporal consistency improvement and fidelity with the per-frame enhanced video, exhibiting a lower memory complexity and comparable time complexity with respect to other state-of-the-art methods for temporal consistency.

Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.

The growing production of digital content and its dissemination across the worldwide web require eficient and precise management. In this context, image quality assessment measures (IQAMs) play a pivotal role in guiding the development of numerous image processing systems for compression, enhancement, and restoration. The structural similarity index (SSIM) is one of the most common IQAMs for estimating the similarity between a pristine reference image and its corrupted variant. The multi-scale SSIM is one of its most popular variants that allows assessing image quality at multiple spatial scales. This paper proposes a two-stage genetic programming (GP) approach to evolve novel multi-scale IQAMs, that are simultaneously more effective and efficient. We use GP to perform feature selection in the first stage, while the second stage generates the final solutions. The experimental results show that the proposed approach outperforms the existing MS-SSIM. A comprehensive analysis of the feature selection indicates that, for extracting multi-scale similarities, spatially-varying convolutions are more effective than dilated convolutions. Moreover, we provide evidence that the IQAMs learned for one database can be successfully transferred to previously unseen databases. We conclude the paper by presenting a set of evolved multi-scale IQAMs and providing their interpretation.

We present a general-purpose framework for the optimization of parametric contrast enhancement algorithms. We first define a regression module for image acceptability, which is based on deep neural features and which is trained on a large dataset of user-expressed preferences. This regression module is then used as the objective function of a Bayesian optimization process, guiding the search for the optimal parameters of a given contrast enhancement algorithm. In our experiments we optimize three different contrast enhancement algorithms of varying levels of complexity. The effectiveness of our optimization framework is experimentally confirmed by evaluating the output of the optimized contrast enhancement algorithms with respect to reference enhanced images.

Abstract Affective computing is a key research topic in artificial intelligence which is applied to psychology and machines. It consists of the estimation and measurement of human emotions. A person’s body language is one of the most significant sources of information during job interview, and it reflects a deep psychological state that is often missing from other data sources. In our work, we combine two tasks of pose estimation and emotion classification for emotional body gesture recognition to propose a deep multi-stage architecture that is able to deal with both tasks. Our deep pose decoding method detects and tracks the candidate’s skeleton in a video using a combination of depthwise convolutional network and detection-based method for 2D pose reconstruction. Moreover, we propose a representation technique based on the superposition of skeletons to generate for each video sequence a single image synthesizing the different poses of the subject. We call this image: ‘history pose image’, and it is used as input to the convolutional neural network model based on the Visual Geometry Group architecture. We demonstrate the effectiveness of our method in comparison with other methods in the state of the art on the standard Common Object in Context keypoint dataset and Face and Body gesture video database.

We present a method for illuminant estimation that exploits a generative adversarial network architecture to generate a spatially-varying illuminant map. This map is then transformed by consensus into a global illuminant estimation, in the form of a single RGB triplet. To this end, different consensus strategies are designed and compared in this paper. The best solution won second place in the 2nd International Illumination Estimation Challenge, specifically for the indoor track.

In this work we present SpliNet, a novel CNNbased method that estimates a global color transform for the enhancement of raw images. The method is designed to improve the perceived quality of the images by reproducing the ability of an expert in the field of photo editing. The transformation applied to the input image is found by a convolutional neural network specifically trained for this purpose. More precisely, the network takes as input a raw image and produces as output one set of control points for each of the three color channels. Then, the control points are interpolated with natural cubic splines and the resulting functions are globally applied to the values of the input pixels to produce the output image. Experimental results compare favorably against recent methods in the state of the art on the MIT-Adobe FiveK dataset. Furthermore, we also propose an extension of the SpliNet in which a single neural network is used to model the style of multiple reference retouchers by embedding them into a user space. The style of new users can be reproduced without retraining the network, after a quick modeling stage in which they are positioned in the user space on the basis of their preferences on a very small set of retouched images.

This paper proposes a framework to train an artifact-free thin cloud removal model using Generative Adversarial Nets (GANs) with thick cloud masks. Satellite images are useful in various applications, however their exploitation is often limited by a presence of clouds. The proposed model can safely remove thin clouds for cloudy images while preserving thick clouds areas without creating undesired artifacts. In order to train the model, we propose a following framework divided in three blocks: generation of thick cloud masks for training images based on texture and spectrum analysis, selection of input-target couples of training images and training of the model using the GAN framework. The use of cloud masks for the training images allows the training of a model for clouds removal, robust to artifact generation in areas with thick clouds. Experimental results show that our model can actually avoid the generation of artifacts, and outperforms the conventional method in terms of SSIM index in testing.

Abstract We present a simple and effective technique for absolute colorimetric camera characterization, invariant to changes in exposure/aperture and scene irradiance, suitable in a wide range of applications including image‐based reflectance measurements, spectral pre‐filtering and spectral upsampling for rendering, to improve colour accuracy in high dynamic range imaging. Our method requires a limited number of acquisitions, an off‐the‐shelf target and a commonly available projector, used as a controllable light source, other than the reflected radiance to be known. The characterized camera can be effectively used as a 2D tele‐colorimeter, providing the user with an accurate estimate of the distribution of luminance and chromaticity in a scene, without requiring explicit knowledge of the incident lighting power spectra. We validate the approach by comparing our estimated absolute tristimulus values (XYZ data in ) with the measurements of a professional 2D tele‐colorimeter, for a set of scenes with complex geometry, spatially varying reflectance and light sources with very different spectral power distribution.

Recent research has widely explored the problem of aesthetics assessment of images with generic content. However, few approaches have been specifically designed to predict the aesthetic quality of images containing human faces, which make up a massive portion of photos in the web. This paper introduces a method for aesthetic quality assessment of images with faces. We exploit three different Convolutional Neural Networks to encode information regarding perceptual quality, global image aesthetics, and facial attributes; then, a model is trained to combine these features to explicitly predict the aesthetics of images containing faces. Experimental results show that our approach outperforms existing methods for both binary, i.e. low/high, and continuous aesthetic score prediction on four different databases in the state-of-the-art.

In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset evaluation setup, and to present competitive results in a comparison with parametric solutions.

In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks. The novelty of the proposed pruning technique is in its differentiability, which allows pruning to be performed during the backpropagation phase of the network training. This enables an end-to-end learning and strongly reduces the training time. The technique is based on a family of differentiable pruning functions and a new regularizer specifically designed to enforce pruning. The experimental results show that the joint optimization of both the thresholds and the network weights permits to reach a higher compression rate, reducing the number of weights of the pruned network by a further 14% to 33 % compared to the current state-of-the-art. Furthermore, we believe that this is the first study where the generalization capabilities in transfer learning tasks of the features extracted by a pruned network are analyzed. To achieve this goal, we show that the representations learned using the proposed pruning methodology maintain the same effectiveness and generality of those learned by the corresponding non-compressed network on a set of different recognition tasks.

Within the field of computer vision, change detection algorithms aim at automatically detecting significant changes occurring in a scene by analyzing the sequence of frames in a video stream. In this paper we investigate how state-of-the-art change detection algorithms can be combined and used to create a more robust algorithm leveraging their individual peculiarities. We exploited genetic programming (GP) to automatically select the best algorithms, combine them in different ways, and perform the most suitable post-processing operations on the outputs of the algorithms. In particular, algorithms' combination and post-processing operations are achieved with unary, binary and n-ary functions embedded into the GP framework. Using different experimental settings for combining existing algorithms we obtained different GP solutions that we termed In Unity There Is Strength. These solutions are then compared against state-of-the-art change detection algorithms on the video sequences and ground truth annotations of the ChangeDetection.net 2014 challenge. Results demonstrate that using GP, our solutions are able to outperform all the considered single state-of-the-art change detection algorithms, as well as other combination strategies. The performance of our algorithm are significantly different from those of the other state-of-the-art algorithms. This fact is supported by the statistical significance analysis conducted with the Friedman test and Wilcoxon rank sum post-hoc tests.

In this paper, we present a three-stage method for the estimation of the color of the illuminant in RAW images. The first stage uses a convolutional neural network that has been specially designed to produce multiple local estimates of the illuminant. The second stage, given the local estimates, determines the number of illuminants in the scene. Finally, local illuminant estimates are refined by non-linear local aggregation, resulting in a global estimate in case of single illuminant. An extensive comparison with both local and global illuminant estimation methods in the state of the art, on standard data sets with single and multiple illuminants, proves the effectiveness of our method.

We propose a new dataset for the evaluation of food recognition algorithms that can be used in dietary monitoring applications. Each image depicts a real canteen tray with dishes and foods arranged in different ways. Each tray contains multiple instances of food classes. The dataset contains 1027 canteen trays for a total of 3616 food instances belonging to 73 food classes. The food on the tray images has been manually segmented using carefully drawn polygonal boundaries. We have benchmarked the dataset by designing an automatic tray analysis pipeline that takes a tray image as input, finds the regions of interest, and predicts for each region the corresponding food class. We have experimented with three different classification strategies using also several visual descriptors. We achieve about 79% of food and tray recognition accuracy using convolutional-neural-networks-based features. The dataset, as well as the benchmark framework, are available to the research community.

We propose a strategy for land use classification, which exploits multiple kernel learning (MKL) to automatically determine a suitable combination of a set of features without requiring any heuristic knowledge about the classification task. We present a novel procedure that allows MKL to achieve good performance in the case of small training sets. Experimental results on publicly available data sets demonstrate the feasibility of the proposed approach.

In this paper, we propose a skin classification method exploiting faces and bodies automatically detected in the image, to adaptively initialize individual ad hoc skin classifiers. Each classifier is initialized by a face and body couple or by a single face, if no reliable body is detected. Thus, the proposed method builds an ad hoc skin classifier for each person in the image, resulting in a classifier less dependent from changes in skin color due to tan levels, races, genders, and illumination conditions. The experimental results on a heterogeneous data set of labeled images show that our proposal outperforms the state-of-the-art methods, and that this improvement is statistically significant.

In this work we design an adaptive color constancy algorithm that, exploiting the skin regions found in faces, is able to estimate and correct the scene illumination. The algorithm automatically switches from global to spatially varying color correction on the basis of the illuminant estimations on the different faces detected in the image. An extensive comparison with both global and local color constancy algorithms is carried out to validate the effectiveness of the proposed algorithm in terms of both statistical and perceptual significance on a large heterogeneous data set of RAW images containing faces.

In this work, we investigate how illuminant estimation can be performed exploiting the color statistics extracted from the faces automatically detected in the image. The proposed method is based on two observations: first, skin colors tend to form a cluster in the color space, making it a cue to estimate the illuminant in the scene; second, many photographic images are portraits or contain people. The proposed method has been tested on a public dataset of images in RAW format, using both a manual and a real face detector. Experimental results demonstrate the effectiveness of our approach. The proposed method can be directly used in many digital still camera processing pipelines with an embedded face detector working on gray level images.

Printer characterization usually requires many printer inputs and corresponding color measurements of the printed outputs. In this brief, a sampling optimization for printer characterization on the basis of direct search is proposed to maintain high color accuracy with a reduction in the number of characterization samples required. The proposed method is able to match a given level of color accuracy requiring, on average, a characterization set cardinality which is almost one-fourth of that required by the uniform sampling, while the best method in the state of the art needs almost one-third. The number of characterization samples required can be further reduced if the proposed algorithm is coupled with a sequential optimization method that refines the sample values in the device-independent color space. The proposed sampling optimization method is extended to deal with multiple substrates simultaneously, giving statistically better colorimetric accuracy (at the α = 0.05 significance level) than sampling optimization techniques in the state of the art optimized for each individual substrate, thus allowing use of a single set of characterization samples for multiple substrates.

One of the most complete techniques that can be used to digitize an artefact is to generate its photo-textured 3D model, which combines high precision metrical information with a faithful color description of the object surfaces. In this work we focus on how to obtain reliable color information by colorimetrically characterizing the color sensors of the imaging device. To this end, a novel target based characterization procedure is proposed that exploits empirical polynomial modeling for colorimetric data estimation. Experimental results are reported and discussed.

In this work, we investigate how illuminant estimation techniques can be improved, taking into account automatically extracted information about the content of the images. We considered indoor/outdoor classification because the images of these classes present different content and are usually taken under different illumination conditions. We have designed different strategies for the selection and the tuning of the most appropriate algorithm (or combination of algorithms) for each class. We also considered the adoption of an uncertainty class which corresponds to the images where the indoor/outdoor classifier is not confident enough. The illuminant estimation algorithms considered here are derived from the framework recently proposed by Van de Weijer and Gevers. We present a procedure to automatically tune the algorithms' parameters. We have tested the proposed strategies on a suitable subset of the widely used Funt and Ciurea dataset. Experimental results clearly demonstrate that classification based strategies outperform general purpose algorithms.

Three-dimensional data generated from range scanners is usually composed of a huge amount of information. Simplification and compression techniques must be adopted in order to reduce transmission or processing time and to allow real-time visualization. In this paper we propose an approach to semantic simplification of triangular meshes representing faces. The algorithm is aimed to preserve facial features and can be used especially for face recognition systems purposes. In a first phase we detect salient regions using a 3D face detector based on curvature analysis and holistic classification. In a second phase, vertex decimation is applied to the mesh with different decimation parameters for salient and non salient-regions. We have tested our algorithm on a set of 150 acquisitions obtaining good visual quality meshes with approximately 90% or more of decimated vertexes

This paper presents an innovative restoration strategy which allows for an effective recognition of 3D faces, even when they are partially occluded by unforeseen, extraneous objects such as scarves, hats, glasses, and so on. First, the occluded regions are detected by considering their effects on the projections of the faces in a suitable face space; the non-occluded regions are then used to restore the missing information. Any recognition algorithm can be applied as usual to restored faces. This restoration strategy led to very satisfactory results on a test set of 52 three-dimensional faces presenting various kinds of occlusions

We introduce software architecture for embedded, real-time monitoring systems. The architecture is based both upon sound software engineering principles and industrial experience in the application domain.

A new approach for selective edge enhancement using unsharp masking is presented. This is based on the premise that biological vision and image reproduction share common principles. In the traditional approach the high frequency components of the image are emphasized, adding to the signal a constant fraction of its high-pass filtered version. The presence of a linear high-pass filter makes the system extremely sensitive to noise. In our approach, the high frequencies added to input image are weighted by a topographic map corresponding to visually salient regions, obtained by a neurodynamical model of visual attention. In this way, the unsharp masking algorithm becomes local and adaptive, enhancing differently the edges according to human perception.

The large size of multispectral data files is currently a major issue in multispectral imaging. The transmission of multispectral data over networks, as well as the storage of large archives, are strongly limited, so that a clear need for good compression methods arises. In this paper, we explore the possibility of loss-less compression for multispectral data through a number of approximation methods that operate on the spectral domain. To evaluate the performance of these methods, we apply them to a representative spectra database, and consider the corresponding decrease in information entropy as well as the classical file size ratio.

We propose a new approach to select the representative frames for video summarization. The representative frames are selected based on the results of the analysis of the events depicted in the shot in terms of regions of interest (ROIs). These ROIs are obtained from a biologically based computational model of visual attention. To select the video frames part of the final visual summary, we exploit an adaptive temporal sampling method that analyzes the visual feature distribution of the ROIs. Preliminary results are presented and discussed.

Annotating photographs with broad semantic labels can be useful in both image processing and content-based image retrieval. We show here how low-level features can be related to semantic photo categories, such as indoor, outdoor and close-up, using decision forests consisting of trees constructed according to CART methodology. We also show how the results can be improved by introducing a rejection option in the classification process. Experimental results on a test set of 4,500 photographs are reported and discussed.

The authors describe the main features of a system supporting the selection of color palettes for qualitative data representation, such as in supervised or unsupervised image classification. Based on visual interaction, the system provides effective tools for browsing the Munsell color space and setting perceptual constraints on the colors, which it then selects automatically. The system is now available for academic and nonprofit purposes.

The need to retrieve visual information from large image and video collections is shared by many application domains. This paper describes the main features of our image search engine Quicklook. Quicklook allows the user to query image and video databases with the aid of example images or a user-made sketch, and progressively refine the system's response by indicating the relevance, or nonrelevance of the retrieved items.

We describe the main features of Quicklook, a prototype system that allows the user to query an image database and progressively refine the system's response by indicating the relevance, or nonrelevance of the retrieved items. Our experience with Quicklook demonstrates that user interaction with the system greatly improves retrieval results, making it possible, with no significant effort by the user, to tune the similarity measure used by the system to the user's notion of image similarity.

The authors define here an approximation of the combination of the forward and reverse Hunt94 color appearance models by means of feed-forward neural networks trained with a back-propagation algorithm on a rather small, standard training set. Experimental results confirm the feasibility of the approach.

Nominal color coding is widely used by the image processing community to represent the output of a classification-segmentation process. In order to automate color-class association we propose a suitable description scheme for both the color set to be used and the image to be coded. Using this description we then define a suitable energy function for Hopfield's neural networks. The objective is to associate more "conspicuous" colors with less "visible" classes, assigning highly contrasting colors to classes with a high "adjacency".

The authors present a noise-robust and general-purpose method for the recognition of graphic symbols in line-drawing images. The method assumes that a noise-free symbol database is available, and that both the model symbols and the processed image have been broken down into elementary structures. Recognition is based on the hypothesis-and-test paradigm. The detection of an elementary structure allows us to use a signature filter, to hypothesize and then recursively reduce a set of matching symbols. Hypothesized symbols are then verified by exploiting distance transform and distance measurement.

Abstract A method is presented which, given the RGB values of a colour stimulus displayed on a given cathode ray tube (to which a CIE XYZ triple corresponds), makes it possible to find a spectral reflectance function characterizing an object colour. Such an object when “illuminated” by a given illuminant produces a metameric spectral power distribution, that is, one with the same XYZ tristimulus values. The method is useful in the colorimetric matching of colours on different media or supports.

The paper describes the design and implementation of an information retrieval system using color as the index in its color image archive. Salient aspects of this approach are the use of direct visual representation of color in querying and of fuzzy set theory to formally represent intrinsic human uncertainty in evaluating the similarity between colors (query interpretation). The steps in which the information retrieval strategy is organized are illustrated, and an example of its application given, showing how the implemented system can be tailored to manage archives of images representing fabric samples.

Abstract A number ofpainting and retouching packages operate much like the artist's traditional canvas andpalette, with electronic toolsfunctioning like their studio counterparts. These programs, while offering an ample set of tools for creating an image, suffer from an intrinsic limitation as regards its modification, which lies in the fact that changes to the shape of objects and those requiring some kind ofpattern recognition are generally difficult and cannot be accomplished automatically. This paper deals with an original methodfor providing a pictorial editor with the “search‐and‐replace” facility, that works rather like text substitution in a word processor. The user defines the search (model) and the replacement (target) patterns by example, i.e. by showing the system the patterns taken from the image or an existing catalogue. The editor then searchesfor objects that match the model's pattern and replaces them with the target one in an automatic or user‐controlled mode. The method is based on a model‐driven matching technique, capable of measuring the similarity of objects that are partially occluded or transformed by translation, rotation, or change of scale. Salient features of the method are its robustness and the limited number of parameters needed to adapt the search procedure to different application contexts. A search‐and‐replace function used for pattern‐editing in the field of textile design, is presented as a working example.

In image analysis, the concept of similarity has been widely explored and various measures of similarity, or of distance, have been proposed that yield a quantitative evaluation. There are cases, however, in which the evaluation of similarity should reproduce the judgment of a human observer based mainly on qualitative and, possibly, subjective appraisal of perceptual features. This process is best modeled as a cognitive process based on knowledge structures and inference strategies, able to incorporate the human reasoning mechanisms and to handle their inherent uncertainties. This articlea proposes a general strategy for similarity evaluation in image analysis considered as a cognitive process. A salient aspect is the use of fuzzy logic propositions to represent knowledge structures, and fuzzy reasoning to model inference mechanisms. Specific similarity evaluation procedures are presented that demonstrate how the same general strategy can be applied to different image analysis problems. © 1993 John Wily & Sons, Inc.

The paper shows how unordered or ordered color sets (color scales) can be generated in a device-independent color space and used for data representation in a computer-aided color coding system based on fundamental principles of human vision.>