Abstract Anomaly detection is a method employed to identify data points or patterns that significantly deviate from expected or normal behaviour within a dataset. This approach aims to detect observations regarded as unusual, erroneous, anomalous, rare, or potentially indicative of fraudulent or malicious activity. Open‐set recognition, also referred to as open‐set identification or open‐set classification, is a pattern recognition task that extends traditional classification by addressing the presence of unknown or novel classes during the testing phase. This approach highlights a strong connection between anomaly detection and open‐set recognition, as both seek to identify samples originating from unknown classes or distributions. Open‐set recognition methods frequently involve modelling both known and unknown classes during training, allowing for the capture of the distribution of known classes while explicitly addressing the space of unknown classes. Techniques in open‐set recognition may include outlier detection, density estimation, or configuring decision boundaries to better differentiate between known and unknown classes. This special issue calls for original contributions introducing novel datasets, innovative architectures, and advanced training methods for tasks related to visual anomaly detection and open‐set recognition.

Background modelling is a fundamental step for several real‐time computer vision applications that requires security systems and monitoring. An accurate background model helps to detect the activity of moving objects in the video. In this work, the authors have developed a new subspace‐based background‐modelling algorithm using the concept of common vector approach (CVA) with Gram–Schmidt orthogonalisation. Once the background model that involves the common characteristic of different views corresponding to the same scene is acquired, a smart foreground detection and background updating procedure is applied based on dynamic control parameters. A variety of experiments is conducted on different problem types related to dynamic backgrounds. Several types of metrics are utilised as objective measures and the obtained visual results are judged subjectively. It was observed that the proposed method stands successfully for all problem types reported on CDNet2014 dataset by updating the background frames with a self‐learning feedback mechanism.

Due to its intuitive structure and efficient implementation, such as integer wavelets, lifting style wavelets gained high popularity. Following the natural correspondence between subband and lifting filters, this paper proposes a new approach to the design of wavelets indirectly through the optimisation of its corresponding block wavelet transform (BWT). BWT is a matrix transform which is generated from subbands, and it describes the relation between these two transform approaches. The BWT optimisation is achieved by making the matrix close to a particular Karhunen-Loeve transform (KLT) of interest. It has been observed that lifting-style wavelets have their constrains in the BWT matrix structure, therefore the minimisation of the difference between a KLT and a BWT derived from a lifting style wavelet becomes a non-trivial task. This paper briefly describes the vanishing moment and orthogonality constraints of the BWT, and introduces the first attempts to obtain single stage lifting wavelet filters that satisfies the constrained minimisation. Experimental results are provided.

Among various approaches to eye tracking systems, light-reflection based systems with non-imaging sensors, e.g., photodiodes or phototransistors, are known to have relatively low complexity; yet, they provide moderately accurate estimation of the point of gaze. In this paper, a low-computational approach on gaze estimation is proposed using the Eye Touch system, which is a light-reflection based eye tracking system, previously introduced by the authors. Based on the physical implementation of Eye Touch, the sensor measurements are now utilized in low-computational least-squares algorithms to estimate arbitrary gaze directions, unlike the existing light reflection-based systems, including the initial Eye Touch implementation, where only limited predefined regions were distinguished. The system also utilizes an effective pattern classification algorithm to be able to perform left, right, and double clicks based on respective eye winks with significantly high accuracy. In order to avoid accuracy problems for sensitive sensor biasing hardware, a robust custom microcontroller-based data acquisition system is developed. Consequently, the physical size and cost of the overall Eye Touch system are considerably reduced while the power efficiency is improved. The results of the experimental analysis over numerous subjects clearly indicate that the proposed eye tracking system can classify eye winks with 98% accuracy, and attain an accurate gaze direction with an average angular error of about 0.93 °. Due to its lightweight structure, competitive accuracy and low-computational requirements relative to video-based eye tracking systems, the proposed system is a promising human-computer interface for both stationary and mobile eye tracking applications.

Due to the popularity of the prediction concept in time series analysis, predictive coding has been an attractive approach, particularly in lossless image compression. Utilization of prediction in time series not only makes use of residual encoding of the prediction error, but also describes and models the behavior of the underlying process. Unfortunately, this approach seems to have limited most of the scientists in the compression society to focus only to causal (or windowed) predictors, which are fine tuned to particular signal patterns. This work considers the fundamental formulation of finite extent data compression by making use of “adaptive multi-channel” prediction that is constructed by comparing prediction values of separate predictors (called, the multiple predictor cooperation). The deliberately generated channels are observed to have sharp error distributions with different bias centers. These biases are centered in a second pass, to produce plausible experimental predictive compression results.

This study introduces a spatio-temporal lifting based algorithm to be used in compression of video signals. The temporal correlation of consecutive frames causes temporal redundancies, which are subject to lifting-like motion predictive compression. Similarly, neighbouring pixels are correlated within each frame. A method that uses both correlations might be 3D lifting-based decomposition. In this study, block-based motion compensation is added to the classical 3D lifting method. Domain of motion compensation is first selected as free, and then reverse-symmetric. It is observed that reverse-symmetric motion compensation improves the performance of the prediction step in 3D lifting based coding.

In this study, a new eye tracking system, namely Eye Touch, is introduced. Eye Touch is based on an eyeglasses-like apparatus on which IrDA sensitive sensors and IrDA light sources are mounted. Using inexpensive sensors and light sources instead of a camera leads to lower system cost and need for the computation power. A prototype of the proposed system is developed and tested to show its capabilities. Based on the test results obtained, Eye Touch is proved to be a promising human-computer interface system.

Lifting-style implementations of wavelets are widely used in image coders. A two-dimensional (2-D) edge adaptive lifting structure, which is similar to Daubechies 5/3 wavelet, is presented. The 2-D prediction filter predicts the value of the next polyphase component according to an edge orientation estimator of the image. Consequently, the prediction domain is allowed to rotate +/-45 degrees in regions with diagonal gradient. The gradient estimator is computationally inexpensive with additional costs of only six subtractions per lifting instruction, and no multiplications are required.

We present a novel and computationally simple prediction stage in a Daubechies 5/3 - like lifting structure for lossless image compression. In the 5/3 wavelet, the prediction filter predicts the value of an odd-indexed polyphase component as the mean of its immediate neighbors belonging to the even-indexed polyphase components. The new edge adaptive predictor, however, predicts according to a local gradient direction estimator of the image. As a result, the prediction domain is allowed to flip + or -45 degrees with respect to the horizontal or vertical axes in regions with diagonal gradient. We have obtained good compression results with conventional lossless wavelet coders.

In this work, we demonstrate examples about uses of practical image processing techniques over a new interpretation of power quality event data. Power quality event data are 1D data obtained from a real life system with three-phase RL loads, induction motors and varying mechanical loads. First, the new 2D representation is presented. Next, 2D wavelet transform based analysis and compression methods are presented. 2D wavelet transform of the 2D representation enable us to clearly detect power quality events that perturbs the normal operation waveform. Furthermore, the transform coefficients are observed to be more suitable for compression than the conventional 1D wavelet based results that could be found in the literature. Simulations are presented.

A subband decomposition based lossless image compression algorithm based on adaptive methods is described. The decomposition is achieved by a two-channel adaptive filter bank. The resulting coefficients are lossy coded first, and then the residual error between the lossy and error free coefficients are compressed. The locations and the magnitudes of the nonzero coefficients are encoded separately by a hierarchical enumerative coding method. The locations of the nonzero coefficients in child bands are predicted from those in the parent band. The proposed compression algorithm, on the average, provides higher compression ratios than the state-of-the-art methods.

We present a new image denoising method based on adaptive subband decomposition (or adaptive wavelet transform) in which the filter coefficients are updated according to a least mean square (LMS) type algorithm. Adaptive subband decomposition filter banks have the perfect reconstruction property. Since the adaptive filter bank adjusts itself to the changing input environment, denoising is more effective compared to fixed filter banks. Simulation examples are presented.

Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral regions of the original data. However, this approach leads to various artifacts in images having spatially varying characteristics, such as images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure which can be either linear or nonlinear vary according to the nature of the signal. This leads to improved image compression ratios. Simulation examples are presented.

In this work, a subband domain textual image compression method is developed. The document image is first decomposed into subimages using binary subband decompositions. Next, the character locations in the subbands and the symbol library consisting of the character images are encoded. The method is suitable for keyword search in the compressed data. It is observed that very high compression ratios are obtained with this method. Simulation studies are presented.

Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral bands of the original data. However, this approach leads to various artifacts in images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure vary according to the nature of the signal. This leads to higher compression ratios for images containing subtitles compared to fixed filter banks. Simulation examples are presented.

Efficient compression of binary textual images is very important for applications such as document archiving and retrieval, digital libraries and facsimile. The basic property of a textual image is the repetitions of small character images and curves inside the document. Exploiting the redundancy of these repetitions is the key step in most of the coding algorithms. We use a similar compression method in the subband domain. Four different subband decomposition schemes are described and their performance on a textual image compression algorithm is examined. Experimentally, it is found that the described methods accomplish high compression ratios and they are suitable for fast database access and keyword search.

Linear filter banks with critical subsampling and perfect reconstruction (PR) property have received much interest and found numerous applications in signal and image processing. Nonlinear filter bank structures with PR and critical subsampling have been proposed and used in image coding. It is shown that PR nonlinear subband decomposition can be performed using the Galois field (GF) arithmetic. The result of the decomposition of an n-ary (e.g. 256-ary) input signal is still n-ary at different resolutions. This decomposition structure can be utilized for binary and 2/sup k/ (k is an integer) level signal decompositions. Simulation studies are presented.

We propose a novel approach for low bit rate perceptually transparent image compression. It exploits both frequency and spatial visual masking effects and uses a combination of Fourier and wavelet transforms to encode different bands. Frequency domain masking is computed by using a fine to coarse analysis step. Spatial domain masking is computed either by using Girod's (1989) model or a coarse to fine analysis step that accurately computes local contrast. A discrete cosine transform is used in conjunction with frequency domain masking to encode the low frequency bands. The medium and high frequency bands are encoded using spatial domain masking and a wavelet transform. The encoding of these bands is based on a recursive selection of the important edges in each band. It uses cross-band prediction to minimize the bit rate. Experiments show the approach can achieve a very high quality to nearly transparent compression at bit rates of 0.2 to 0.4 bits/pixel.

The wavelet transform provides a multiresolution representation of images. Edges, which are visually important, produce large coefficients across several scales in the wavelet transform domain. By tracking and predicting these edge coefficients across scales in the wavelet transform domain, we can greatly improve the compressed image quality with little degradation in compression ratio. This paper proposes a novel model-based edge tracking and prediction in the wavelet domain. It separates textures from edges and codes them differently. Edges are coded via an edge tracking and prediction, while textures are coded with either ordinary wavelet based image coding techniques or a "wavelet-like" filter bank which is similar to the tuning channels in the human vision system. The coding noise is then coded with a noise modelling. Visual masking models are also used to ensure the compressed image has little or almost no perceptual distortion.

A new class of block transforms is presented. These transforms are constructed from subband decomposition filter banks corresponding to regular wavelets. New transforms are compared to the discrete cosine transform (DCT). Image coding schemes that use the block wavelet transform (BWT) are developed. BWT's can be implemented by fast (O(N log N)) algorithms.>