Conventionally, the point spread function (PSF) is understood as a characteristic function of any optical system. It captures the information about the amount of blur present along all the directions for a point in the scene. However, the dependence of blur on the PSF is in the form of convolution for any object other than a point source present in the scene and hence their relationship is less explicit. The authors propose a blur parameter locus curve (BPLC) as a system representation which has a one to one relationship with blur. BPLC simply is a chart of blur amounts in all directions of a given PSF with respect to the selected measurement function. They further characterise the PSF by decomposing the variation of BPLC across all directions based on the study performed for different possible forms of the blur kernels. Such decomposition provides powerful tools for various analysis. As PSF can be anisotropic, the computation of BPLC becomes an essential intermediate step to obtain the scale map as at the same scale, blur is different in different directions. Furthermore, they demonstrate the use of BPLC to obtain other system characteristics function such as PSF.

The occurrence of motion blur along with defocus blur is a common phenomena in natural images. Usually, these blurs are spatially varying in nature for any general image and estimation of one type of blur is affected by presence of other. In this paper, we propose a novel method to estimate the concurrent defocus and motion blurs in a single image. Unlike the recent methods, which perform well only on simulated conditions or in presence of single type of blur, proposed method works well for real images as well as for compressed images. In this paper, we consider only commonly associated motion and defocus blurs for analysis. Decoupling of motion and defocus blur provides a fundamental tool that can be used for various analysis and applications.

Dehazing is an important process as it can significantly improve the performance of computer vision applications in outdoor environments. The two main requirements that an online dehazing system demands are low processing time and high visual range. The authors present a novel dehazing algorithm based on colour uniformity principle (CUP) which meets the desired requirements of a realtime implementation. Estimation of atmospheric scattering parameter and transmission map forms the key step in dehazing problem. At first, the authors use CUP to generate the transmission map and refine it further by Fast Guided Filter. They estimate the atmospheric scattering parameter with the help of the estimated transmission map. Experimental results show that the quality of dehazed output, produced in real‐time using the proposed method, is comparable with the results achieved by the state of the art techniques. The proposed dehazing method produces reliable dehazed output in varying haze conditions, unlike current methods.

Significant recent developments in 3D display technology have focused on techniques for converting 2D media into 3D. Depth map is an integral part of 2D-to-3D conversion. Combining multiple depth cues results in a more accurate depth map as it compensates for the errors caused by one depth cue as well as its absence by other depth cues. In this paper, we present a novel framework to generate a more accurate depth map for video using defocus and motion cues. The moving objects present in the scene are the source of errors in both defocus and motion-based depth map estimation. The proposed method rectifies these errors in the depth map by integrating defocus blur and motion cues. In addition, it also corrects the errors in other parts of depth map caused by inaccurate estimation of defocus blur and motion. Since the proposed integration approach relies on the characteristics of point spread functions of defocus and motion blur along with their relations to camera parameters, it is more accurate and reliable.

Video summarization is an economical way of representing video contents and is useful for effective and quick browsing of the relevant activity present in the video. Most current video summarization approaches do not provide the correct insight of the events occurring in the video, such as moving the objects in a nonchronological order and tampering with the background and size of the objects. In this paper, we present an approach to create a video summarization that is a precise representation of the video content. First, our approach finds out the salient activities that are taking place in the video using an optimization framework for static and dynamic scenes. Second, the frames with the salient activities are stitched using alpha matting to form a single frame. Third, the summarized frame over multiple video shots obtained by our approach gives superior retrieval performance with image queries while reducing retrieval latency and memory requirement. This paper proposes a single frame-based indexing of the video database instead of multi-frame indexing as it is an interesting and potentially promising approach. Finally, several experiments are carried out to evaluate the proposed approach. The experiments demonstrate that the video summarization produced by our approach gives a realistic view of the events in the video, while the reduction in computational complexity and memory requirement is high.

We propose a novel technique for detection of visual saliency in dynamic video based on video decomposition. The decomposition obtains the sparse features in a particular orientation by exploiting the spatiotemporal discontinuities present in a video cube. A weighted sum of the sparse features along three orthogonal directions determines the salient regions in the video cubes. The weights computed using the frame correlation along three directions are based on the characteristic of human visual system that identifies the sparsest feature as the most salient feature in a video. Unlike the existing methods, which detect the salient region as blob, the proposed approach detects the exact boundaries of salient region with minimum false detection. The experimental results confirm that the detected salient regions of a video closely resemble the salient regions detected by actual tracking of human eyes. The algorithm is tested on different types of video contents and compared with the several state-of-the-art methods to establish the effectiveness of the proposed method.

Roads are the vital mode of transportation for people and goods around the globe and its use has grown dramatically over the years. There is one death every four minutes due to road accidents in the developing nations. This is of deep concern to the entire humanity. Road accident detection and vehicle behavior analysis is of great interest to the research community in intelligent transportation systems. It is very difficult from the state of the art techniques to provide the abstract form of salient parts of accidents from road surveillance videos. To resolve these issues, we present perceptual video summarization techniques to enrich the speed of visualizing the accident content from a stack of videos. The problem of vehicle analysis is formulated as an optimization problem. To the best of our knowledge, this is the first time we solve an accident detection as an optimization problem and filter the frames to be selected, through a single formulation. With the camera in a surrounding infrastructure and capturing a video, we exploited the properties of sub modularity to provide a relevant and condensed key frame summary. We have studied it for various real world traffic surveillance videos comprising of vehicular accidents and thus making it a promising approach.

We propose a new technique for video colorization based on spatiotemporal color propagation in the 3D video volume, utilizing the dominant orientation response obtained from the steerable pyramid decomposition of the video. The volumetric color diffusion from the sources that are marked by scribbles occurs across spatiotemporally smooth regions, and the prevention of leakage is facilitated by the spatiotemporal discontinuities in the output of steerable filters, representing the object boundaries and motion boundaries. Unlike most existing methods, our approach dispenses with the need of motion vectors for interframe color transfer and provides a general framework for image and video colorization. The experimental results establish the effectiveness of the proposed approach in colorizing videos having different types of motion and visual content even in the presence of occlusion, in terms of accuracy and computational requirements.

The enormous growth of video content in recent times has raised the need to abbreviate the content for human consumption. Thus, there is a need for summaries of a quality that meets the requirements of human users. This also means that the summarization must incorporate the peculiar features of human perception. We present a new framework for video summarization in this paper. Unlike many available summarization algorithms that utilize only statistical redundancy, we introduce for the first time the features of the human visual system within the summarization framework itself to allow for the emphasis of perceptually significant events while simultaneously eliminating perceptual redundancy from the summaries. The subjective and objective evaluation scores have evaluated the framework.

Estimation of salient regions in an input video is an active area of research due to its wide applications. In this paper, we propose a novel algorithm to estimate the eye gaze movement in a video using motion, color and structural cues with minimum outliers. The algorithm is generalized to capture salient information for the videos taken under different camera motions. The entire algorithm is parallelizable and ensures faster estimation of salient regions. Using different standard datasets, the estimations of proposed algorithm are compared with state-of-the-art approaches. It is observed that the proposed method produces estimations closer to the ground-truth eye tracker data with minimum outliers.

Images captured in presence of fog, haze or snow usually suffer from poor contrast and visibility. In this paper we propose a novel dehazing method to increase visibility from a single view without using any prior knowledge about the outdoor scene. The proposed method estimates a visibility map of the scene from the input image and uses stochastic iterative algorithm to remove fog and haze. The method can be applied to color and grayscale images. Experimental results show that the proposed algorithm outperforms most of the state-of-the-art algorithms in terms of contrast, colorfulness and visibility.

In this paper, a new algorithm is proposed for fast kernel density estimation (FKDE), based on principal direction divisive partitioning (PDDP) of the data space. A new framework is also developed to apply FKDE algorithms (both proposed and existing), within nonparametric noncausal Markov random field (NNMRF) based texture synthesis algorithm. The goal of the proposed FKDE algorithm is to use the finite support property of kernels for fast estimation of density. It has been shown that hyperplane boundaries for partitioning the data space and principal component vectors of the data space are two requirements for efficient FKDE. The proposed algorithm is compared with the earlier algorithms, with a number of high-dimensional data sets. The error and time complexity analysis, proves the efficiency of the proposed FKDE algorithm compared to the earlier algorithms. Due to the local simulated annealing, direct incorporation of the FKDE algorithms within the NNMRF-based texture synthesis algorithm, is not possible. This work proposes a new methodology to incorporate the effect of local simulated annealing within the FKDE framework. Afterward, the developed texture synthesis algorithms have been tested with a number of different natural textures, taken from a standard database. The comparison in terms of visual similarity and time complexity, between the proposed FKDE based texture synthesis algorithm with the earlier algorithms, show the efficiency.

Colorization is a computer-aided process of adding color to a grayscale image or video. The task of colorizing a grayscale image involves assigning three dimensional (RGB) pixel values to an image which varies along only one dimension (luminance or intensity). Since different colors may have the same luminance value but vary in hue or saturation, mapping between intensity and color is not unique, and colorization is ambiguous in nature, requiring some amount of human interaction or external information. In this paper we propose a semi-automatic process for colorization where the user indicates how each region should be colored by putting the desired color marker in the interior of the region. The algorithm based on the position and color of the markers, segments the image and colors it. In order to colorize videos, few reference frames are chosen manually from a set of automatically generated key frames and colorized using the above marker approach and their chrominance information is then transferred to the other frames in the video using a color transfer technique making use of motion estimation. The colorization results obtained are visually very good. In addition the amount of manual intervention is reduced since the user only has to apply color markers on few selected reference frames and the proposed algorithm colors the entire video sequence.

In this paper we introduce a new neighborhood system based Nonparametric Markov Random Field (NMRF) model for texture synthesis. The objective of this work is to generate minimal neighborhood system, in MRF based texture synthesis algorithms. Compared to the earlier neighborhood system (circular), the proposed one is not isometric and has fewer number of pixels within the neighborhood.This reduction in the number of neighborhood pixels does not degrade the synthesis results compared to the existing neighborhood systems.Two popular texture synthesis algorithms (based on nonparametric MRF) have been tested with the new neighborhood system for two standard texture databases.The results establish the efficacy of the proposed neighborhood system in terms of computational gain, with no visible degradation compared to the earlier systems.

Markov random field (MRF) based sampling method is popular for synthesizing natural textures. The main drawback of the synthesis procedure is the large computational complexity involved. In this paper, we propose an approximation of the conditional density description for the reduction of computational complexity required in sampling texture pixels from the conditional density. Assuming, Y isin Lambda, and X isin Lambdad, we in this work studied the approximation of the conditional density function P(Y|X) as P(Y|thetastX), where thetas isin Rd, is a unit vector. We have also shown that the classical gradient based optimization method is not suitable for finding the solution of thetas. We have estimated thetas using genetic algorithm. The perceptual (visual) similarity and neighborhood similarity measures between the textures synthesized using the full conditional description and approximated description, are shown for validating the method developed.

Fusion is basically extraction of best of inputs and conveying it to the output. In this paper, we present an image fusion technique using the dual tree complex wavelet transform (DTCWT). We have proposed novel masks to extract information from the decomposed structure using DTCWT. The main goal of this paper is to introduce a new approach to fuse multimodality images using dual tree complex wavelet transform. Experiment results show that the proposed fusion method based on complex wavelet transform is remarkably better than the fusion method based on classical discrete wavelet transform. This method is relevant to visual sensitivity and tested by merging multisensor, multispectral and defocused images apart from medical images (CT and MR images). Fusion is achieved through the formation of a fused pyramid using the DTCWT coefficients from the decomposed pyramids of the source images. The fused image is obtained through conventional inverse dual tree complex wavelet transform reconstruction process. Results obtained using the proposed method show a significant reduction of distortion

Block motion estimation is one of the most significant and computationally demanding component in video encoding. Most of the search algorithms start their search from the center of the search window and speed up the process by limiting the search locations. The search however, may get trapped in a local minimum on the error surface resulting in suboptimal quality. Another drawback is that the blocks located on the edges of moving objects are not well estimated, to which the human visual system is very sensitive. The technique proposed in this paper employs features in a macroblock to place the initial search point near to the global minimum of the error surface. Consequently, a fast and more efficient motion estimation algorithm is developed. The computational feasibility, better objective quality and the fact that it works as a simple extension to the existing search algorithms are the main advantages of the algorithm.

The paper describes a new method for texture analysis and synthesis using angular wavelet frames (AWF). The objective of using AWF is to improve the performance of existing wavelet based classification algorithms for textured images having dominant angular components, as well as the synthesis algorithms for textures with long range and non linear spatial correlation. The 2D AWF is constructed by frequency transformations of the prototype 1D perfect reconstruction filter bank (PRFB). Encouraging results have been obtained by this method.

This paper discusses the implementation of a very low bit rate video codec (coder-decoder) which achieves a bit rate of as low as about 25 Kbps for test sequences having frame rate of 7 fps and frame size of 256×256 pixels. We have employed techniques like Comer matching and tracking for motion prediction that is instrumental in reducing the bitrate and the optimized rainfalling watershed algorithm results in better and faster image segmentation. We also discuss the results of incorporating Smart (yet simple and flexible) Error Resiliency features like Reversible Variable Length Coding and Resynchronization Markers into out very low bit-rate video codec.

One of the challenging problems in forming a multimedia database is the organisation of visual information. Since visual media requires large amount of storage and processing, there is a need to efficiently index, store and retrieve the visual information from multimedia database. This paper presents a unique technique for robust and efficient video indexing by analyzing the video sequence directly in compressed domain. Abrupt scene changes and special editing effects such as dissolves, fades etc. are easily detected using only DC images of I and P frames and the macroblock information of P and B frames. The proposed scheme is computationally less complex as compared to the conventional methods, while also being robust to camera or/and object motion and flashlight.

This paper draws significant attention to the impending need of media copyright protection for the upcoming content based video indexing and retrieval. We propose a unique technique for joint indexing and watermarking of a compressed bitstream. The method employs video segmentation of the compressed bitstream, followed by extraction of key frames. The key frames are the representative frames of the video. The features of the extracted key frames are used both for watermarking as well as an index for retrieval. Watermarking is done on luminance and chrominance samples in the compressed domain bitstream considering the sensitivities of the human visual system(HVS) to luminance and color components. This increases the robustness of the system without increasing the bit rate of the compressed video. The proposed scheme reduces computational complexity to a great extent, and also reduces the storage space.

It is known that the convolution back-projection (CBP) operator used for reconstruction of images from its 1D projections has a non-local filter that requires global projection data. The exposure time of the object to harmful radiation is thereby increased. It has been proved that the filtering operation done on a chosen wavelet function instead of the projections leads to a localised filter and minimises the number of projections needed to reconstruct the region of interest (ROI) in the image. This concept was extended using two dimensional and one dimensional multiresolution analysis filter banks, in which the filters are combined with the non-local CBP filter to get short length filters. In this paper it is proved that the presence of unwanted information in all previous algorithms is due to aliasing and a new algorithm is proposed for reconstruction without any aliasing. The proposed scheme is implemented using Shepp-Logon phantom head and the performance is compared.