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Action recognition from videos is important due to its numerous applications in real life scenarios. Most commonly action recognition is used for surveillance but it is also used in other applications such as entertainment and gaming. Moreover, due to high demand of automation and easy availability of higher processing power, researchers are aiming for better ways to accurately classify human actions using videos. Different methods have been proposed in literature in order to identify actions. However, most of these methods suffer from limitations such as implementation complexity and inability to recognize complex actions. In this paper, we propose a novel action recognition technique which is easy to implement and is robust to complex actions. While there are different methods to capture motion, trajectories based methods have proven to be an excellent way to represent motion in videos. Inspired by that, we sampled dense points in different frames of the video to track down their displacement information to encode the trajectories. We used dense trajectories along with oriented gradients and motion boundary information as features. We use codebook generated using Gaussian mixture model and Fisher vector to encode our features. Finally, after reducing the dimensionality of our data, we use linear Support Vector Machine to identify the actions. The experimental evaluations performed on a recent benchmark action recognition dataset showed that our method is able to achieve encouraging results compared to other competing methods.

Image fusion is a process that integrates similar types of images collected from heterogeneous sources into one image in which the information is more definite and certain. Hence, the resultant image is anticipated as more explanatory and enlightening both for human and machine perception. Different image combination methods have been presented to consolidate significant data from a collection of images into one image. As a result of its applications and advantages in variety of fields such as remote sensing, surveillance, and medical imaging, it is significant to comprehend image fusion algorithms and have a comparative study on them. This paper presents a review of the present state-of-the-art and well-known image fusion techniques. The performance of each algorithm is assessed qualitatively and quantitatively on two benchmark multi-focus image datasets. We also produce a multi-focus image fusion dataset by collecting the widely used test images in different studies. The quantitative evaluation of fusion results is performed using a set of image fusion quality assessment metrics. The performance is also evaluated using different statistical measures. Another contribution of this paper is the proposal of a multi-focus image fusion library, to the best of our knowledge, no such library exists so far. The library provides implementation of numerous state-of-the-art image fusion algorithms and is made available publicly at project website.

Malaria is caused by Plasmodium parasite. It is transmitted by female Anopheles bite. Thick and thin blood smears of the patient are manually examined by an expert pathologist with the help of a microscope to diagnose the disease. Such expert pathologists may not be available in many parts of the world due to poor health facilities. Moreover, manual inspection requires full concentration of the pathologist and it is a tedious and time consuming way to detect the malaria. Therefore, development of automated systems is momentous for a quick and reliable detection of malaria. It can reduce the false negative rate and it can help in detecting the disease at early stages where it can be cured effectively. In this paper, we present a computer aided design to automatically detect malarial parasite from microscopic blood images. The proposed method uses bilateral filtering to remove the noise and enhance the image quality. Adaptive thresholding and morphological image processing algorithms are used to detect the malaria parasites inside individual cell. To measure the efficiency of the proposed algorithm, we have tested our method on a NIH Malaria dataset and also compared the results with existing similar methods. Our method achieved the detection accuracy of more than 91% outperforming the competing methods. The results show that the proposed algorithm is reliable and can be of great assistance to the pathologists and hematologists for accurate malaria parasite detection.

Real estate objects are three dimensional, however they are usually dealt in one or two dimensions. In modern construction industry, the land ownership is counted above as well as beneath the earth surface which requires the measurement, ownership documentation and the rights as volume (3D). Major socioeconomic issues of Pakistan are high population growth rate, high rate of urbanization, industrialization and alarming rate of conversion of agricultural land in to non-agricultural uses. During the past few decades, the trend of urbanization resulted in huge horizontal expansion of the cities which raised many problems, e.g., traffic conjunction, security and slums. To address these problems, vertical growth of cities was emphasized and all the major cities of Pakistan were populated with skyscrapers but its legal, technical and the institutional perspectives were not planned which led to the insecure land tenure and failure to control the horizontal expansion of cities. This paper analyzes the current land record system in urban areas of Punjab and identifies its limitations.

Multiview video plus depth (MVD) is the most popular 3D video format due to its efficient compression and provision for novel view generation enabling the free-viewpoint applications. In addition to color images, MVD format provides depth maps which are exploited to generate intermediate virtual views using the depth image-based rendering (DIBR) techniques. Compression affects the quality of the depth maps which in turn may introduce various structural and textural distortions in the DIBR-synthesized images. Estimation of the compression-related distortion in depth maps is very important for a high-quality 3D experience. The task becomes challenging when the corresponding reference depth maps are unavailable, e.g., when evaluating the quality on the decoder side. In this paper, we present a no-reference quality assessment algorithm to estimate the distortion in the depth maps induced by compression. The proposed algorithm exploits the depth saliency and local statistical characteristics of the depth maps to predict the compression distortion. The proposed ‘depth distortion evaluator’ (DDE) is evaluated on depth videos from standard MVD database compressed with the state-of-the-art high-efficiency video coding at various quality levels. The results demonstrate that DDE can be used to effectively estimate the compression distortion in depth videos.

Multi-focus image fusion has emerged as an important research area in information fusion. It aims at increasing the depth-of-field by extracting focused regions from multiple partially focused images, and merging them together to produce a composite image in which all objects are in focus. In this paper, a novel multi-focus image fusion algorithm is presented in which the task of detecting the focused regions is achieved using a Content Adaptive Blurring (CAB) algorithm. The proposed algorithm induces non-uniform blur in a multi-focus image depending on its underlying content. In particular, it analyzes the local image quality in a neighborhood and determines if the blur should be induced or not without losing image quality. In CAB, pixels belonging to the blur regions receive little or no blur at all, whereas the focused regions receive significant blur. Absolute difference of the original image and the CAB-blurred image yields initial segmentation map, which is further refined using morphological operators and graph-cut techniques to improve the segmentation accuracy. Quantitative and qualitative evaluations and comparisons with current state-of-the-art on two publicly available datasets demonstrate the strength of the proposed algorithm. - 2018 Elsevier B.V. This work was made possible by NPRP grant number NPRP 7-1711-1-312 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

This paper presents a novel cross-view gait recognition technique based on the spatiotemporal characteristics of human motion. We propose a deep fully-connected neural network with unsupervised learning which transfers the gait descriptors from multiple views to the single canonical view. The proposed non-linear network learns a single model for all videos captured from different viewpoints and finds a shared high-level virtual path to map them on a single canonical view. Therefore, the model does not require any labels or viewpoint information in the learning phase. The network is learned only once using the spatiotemporal motion features of the gait sequences from several viewpoints, later it is used to construct the cross-view gait descriptors for the gallery and the probe sets. The descriptors are classified using simple linear support vector machine. Experiments carried out on the benchmark cross-view gait dataset, CASIA-B, and comparisons with the state-of-the-art demonstrate that the proposed method outperforms the existing cross-view gait recognition algorithms.

Three Dimensional (3D) image quality assessment is a challenging problem as compared to 2D images due to their different nature of acquisition, representation, coding, and display. The additional dimension of depth in multiview video plus depth (MVD) format is exploited to obtain images at novel intermediate viewpoints using depth image based rendering (DIBR) techniques, enabling 3D television and free-viewpoint television (FTV) applications. Depth maps introduce various quality artifacts in the DIBR-synthesized (virtual) images. In this paper, we propose a novel methodology to evaluate the quality of synthesized views in absence of the corresponding original reference views. It computes the statistical characteristics of the side views from whom the virtual view is generated, and fuses this information to estimate the statistical characteristics of the cyclopean image which are compared to those of the synthesized image to evaluate its quality. In addition to texture images, the proposed algorithm also considers the depth maps in evaluating the quality of the synthesized images. The algorithm blends two quality metrics, one estimating the texture distortion in the synthesized texture image induced by compression, transmission, 3D warping, or other causes and the second one determining the distortion of the depth maps. The two metrics are combined to obtain an overall quality assessment of the synthesized image. The proposed Synthesized Image Quality Metric (SIQM) is tested on the challenging MCL-3D and SIAT-3D datasets. The evaluation results show that the proposed metric significantly improves over state-of-the-art 3D image quality assessment algorithms.

The future of novel 3D display technologies largely depends on the design of efficient techniques for 3D video representation and coding. Recently, multiple view plus depth video formats have attracted many research efforts since they enable intermediate view estimation and permit to efficiently represent and compress 3D video sequences. In this paper, we present spatiotemporal occlusion compensation with panorama view (STOP), a novel 3D video coding technique based on the creation of a panorama view and occlusion coding in terms of spatiotemporal offsets. The panorama picture represents the most of the visual information acquired from multiple views using a single virtual view, characterized by a larger field of view. Encoding the panorama video with state-of-the-art HECV and representing occlusions with simple spatiotemporal ancillary information STOP achieves high-compression ratio and good visual quality with competitive results with respect to competing techniques. Moreover, STOP enables free viewpoint 3D TV applications whilst allowing legacy display to get a bidimensional service using a standard video codec and simple cropping operations.

Multiview video plus depth (MVD) is the most popular 3D video format where the texture images contain the color information and the depth maps represent the geometry of the scene. The depth maps are exploited to obtain intermediate views to enable 3D-TV and free-viewpoint applications using the depth image based rendering (DIBR) techniques. DIBR is used to get an estimate of the intermediate views but has to cope with depth errors, occlusions, imprecise camera parameters, re-interpolation, to mention a few issues. Therefore, being able to evaluate the true perceptual quality of synthesized images is of paramount importance for a high quality 3D experience. In this paper, we present a novel algorithm to assess the quality of the synthesized images in the absence of the corresponding references. The algorithm uses the original views from which the virtual image is generated to estimate the distortion induced by the DIBR process. In particular, a block-based perceptual feature matching based on signal phase congruency metric is devised to estimate the synthesis distortion. The experiments worked out on standard DIBR synthesized database show that the proposed algorithm achieves high correlation with the subjective ratings and outperforms the existing 3D quality assessment algorithms.

Sediment yield is the amount of erosional debris from drainage basin deposited in reservoirs. The economic life of storage reservoir depends upon the estimation of the time it takes for the reservoir to be filled with the deposition of sediments. This research is based on assessing the sediment yield in Rawal Dam catchment by using Soil and Water Assessment Tool (SWAT) model. Digital Elevation Model (DEM), land use maps, soil maps, and weather data of the study watershed were used as input to SWAT model. Monthly sedimentation data of year 2010 and discharge data from 1998 to 2005 is being used for model calibration and validation, respectively. Whereas simulations are being generated from 1998 to 2011 for both sedimentation and discharge. Modified Universal Soil Loss Equation (MUSLE) was used for the estimation of sediment yield. The Nash and Sutcliffe coefficient of the model was found to be 0.79 which depicts its effectiveness. After the estimation of the sediment yield and discharge by using SWAT model, double mass curve was used to evaluate the sedimentation rate. The rate of sediment transport can be reduced by the construction of check dams. Various sites have also been proposed for check dams construction to prevent the sediments transported into the Rawal Catchment.

In free-viewpoint television (FTV) framework, due to hardware and bandwidth constraints, only a limited number of viewpoints are generally captured, coded and transmitted; therefore, a large number of views needs to be synthesized at the receiver to grant a really immersive 3D experience. It is thus evident that the estimation of the quality of the synthesized views is of paramount importance. Moreover, quality assessment of the synthesized view is very challenging since the corresponding original views are generally not available either on the encoder (not captured) or the decoder side (not transmitted). To tackle the mentioned issues, this paper presents an algorithm to estimate the quality of the synthesized images in the absence of the corresponding reference images. The algorithm is based upon the cyclopean eye theory. The statistical characteristics of an estimated cyclopean image are compared with the synthesized image to measure its quality. The prediction accuracy and reliability of the proposed technique are tested on standard video dataset compressed with HEVC showing excellent correlation results with respect to state-of-the-art full reference image and video quality metrics.

Multiview videos plus depth (MVD) is a popular 3D video representation where pixel depth information is exploited to generate additional views to provide 3D experience. Quality assessment of MVD data is of paramount importance since the latest research results show that existing 2D quality metrics are not suitable for MVD. This paper focuses on depth quality assessment and presents a novel algorithm to estimate the distortion in depth videos induced by compression. The proposed algorithm is no reference and does not require any prior training or modeling. The proposed method is based solely on the statistical analysis of the compression sensitive pixels of depth images. The experimental results worked out on a standard MVD dataset show that the proposed algorithm exhibits a very high correlation with conventional full-reference metrics.

Depth image based rendering is a well-known technology for the generation of virtual views in between a limited set of views acquired by a cameras array. Intermediate views are rendered by warping image pixels based on their depth. Nonetheless, depth maps are usually imperfect as they need to be estimated through stereo matching algorithms; moreover, for representation and transmission requirements depth values are obviously quantized. Such depth representation errors translate into a warping error when generating intermediate views thus impacting on the rendered image quality. We observe that depth errors turn to be very critical when they affect the object contours since in such a case they cause significant structural distortion in the warped objects. This paper presents an algorithm to improve the visual quality of the synthesized views by enforcing the shape of the edges in presence of erroneous depth estimates. We show that it is possible to significantly improve the visual quality of the interpolated view by enforcing prior knowledge on the admissible deformations of edges under projective transformation. Both visual and objective results show that the proposed approach is very effective.

The success of 3D and free-viewpoint television largely depends on the efficient representation and compression of 3D video in addition to viable rendering methods. This paper presents a novel 3D video coding technique based on the creation of a panorama view to compact the information of a stereoscopic pair. The panorama view represents the information that would be visible to a virtual camera with a larger field of view embracing all the available views. The information in the panorama view is then used to estimate any intermediate view using depth image based rendering. Furthermore, to fill the disocclusions in the reconstructed view a directional depth aided fast marching inpainting technique is presented. The panorama view and corresponding depth map are amenable to standard video compression. In this paper we show that using the novel HEVC standard the proposed 3D video format can be compressed very efficiently.

Three-dimensional video has gained much attention during the last decade due its vast applications in cinema, television, animation and virtual reality. The design of intermediate view synthesis algorithms that are efficient both in terms of computational complexity and visual quality is a paramount goal in the fields of 3D free view point television and displays. This papers focuses on the design of a low complexity view synthesis algorithm that produces better quality of the virtual image. A novel view synthesis technique to create a virtual view from two video sequences with corresponding depths is proposed. The technique employs low complexity integer pixel precision warping and a novel approach for hole filling based on inverse mapping. The proposed technique is tested over a number of video sequences and compared with existing state of the art methods, yielding excellent results both in terms of signal to noise ratio and visual quality.

Depth image based rendering of intermediate views with high visual quality remains a challenging goal in presence of estimated and quantized depth values. Among the other rendering artifacts we observed that edges are usually affected by significant warping errors. In particular, because of depth estimation inaccuracy around object boundaries the edges may completely loose their original shape during the warping process. Nonetheless, edges represent one of the most important cues for the human visual system. In this paper a novel technique aiming at improving the edge rendering is presented. As opposed to previous approaches, the technique exploits only texture information, thus avoiding possible errors in depth estimation. The idea is based on the enforcement of prior knowledge of the edge shape under projective transformation. The proposed algorithm works in two steps: first the damaged edges of the warped image are detected, then these latter are corrected so as to better approximate their shape in the reference view. Finally the corrected edges are rendered within the intermediate image without introducing noticeable texture artifacts. The proposed algorithm has been tested on a variety of standard video sequences exhibiting excellent results in terms of rendered image visual quality.

Most human detection algorithms in depth images perform well in detecting and tracking the movements of a single human object. However, their performance is rather poor when the person is occluded by other objects or when there are multiple humans present in the scene. In this paper, we propose a novel human detection technique which analyzes the edges in depth image to detect multiple people. The proposed technique detects a human head through a fast template matching algorithm and verifies it through a 3D model fitting technique. The entire human body is extracted from the image by using a simple segmentation scheme comprising a few morphological operators. Our experimental results on three large human detection datasets and the comparison with the state-of-the-art method showed an excellent performance achieving a detection rate of 94.53% with a small false alarm of 0.82%.