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It is challenging to extend the support region of state-of-the-art local edge-preserving filtering approaches to the entire input image on account of huge memory cost and heavy computational burden. In this paper, we propose an O(N) time recursive non-local edge-aware filter. A novel graph and a linear time algorithm are presented to effectively propagate information across the entire image. In this graph, information is propagated along all directions to avoid visual artifacts. Both the intensity similarity and the spatial affinity are utilized to estimate the similarity of neighboring pixels, and the distance of any two pixels is the length of the path between these two pixels on a binary tree. Specifically, the input image is filtered at four directions, namely left-to-right, right-to-left, top-to-bottom and bottom-to-top. In each pass, the un-normalized output and the normalization constant of the root node are computed recursively from leaf nodes to the root node on a binary tree in linear time. The filtering output is the average of the outputs for these four directions. A comparison with other non-local edge-aware filters is presented to show the advantages of our approach. Extensive experiments demonstrate that our recursive non-local edge-preserving filter is effective in a variety of computer vision and image processing applications, including edge-aware filtering, detail enhancement, stylization, and stereo matching. [ABSTRACT FROM AUTHOR]

Objective image quality metrics (IQMs) potentially benefit from the addition of visual saliency. However, challenges to optimizing the performance of saliency-based IQMs remain. A previous eye-tracking study has shown that gaze is concentrated in fewer places in images with highly salient features than in images lacking salient features. From this, it can be inferred that the former are more likely to benefit from adding a saliency term to an IQM. To understand whether these ideas still hold when using computational saliency instead of eye-tracking data, we first conducted a statistical evaluation using 15 state-of-the-art saliency models and 10 well-known IQMs. We then used the results to devise an algorithm, which adaptively incorporates saliency in IQMs for natural scenes, based on saliency dispersion. Experimental results demonstrate that this can give significant improvements. [ABSTRACT FROM AUTHOR]

The use of local features for image representation has become popular in recent years. Local features are often used in the bag-of-visual-words scheme. Although proven effective, this method still has two drawbacks. First, local regions from which local features are extracted are not discriminative enough for visual tasks. Hence, the combination of local features is necessary. Second, the semantic gap between visual features and human perception also hinders the performance. To address these two problems, in this paper, we propose a novel contextual exemplar classifier-based method for image representation and apply it for classification tasks. Each exemplar classifier is trained to separate one training image from the other images of different classes. We partition each image into a number of regions and use the responses of these exemplar classifiers as the image region’s representation. The contextual relationship is then modeled using mixture Dirichlet distributions. A bilayer model is used to predict image classes with L2 constraints. Experimental results on the Natural Scene, Caltech-101/256, Flower-17/102, and SUN-397 data sets show that the proposed method is able to outperform the state-of-the-art local feature-based methods for image classification. [ABSTRACT FROM PUBLISHER]

Saliency detection has been extensively studied due to its promising contributions for various computer vision applications. However, most existing methods are easily biased toward edges or corners, which are statistically significant, but not necessarily relevant. Moreover, they often fail to find salient regions in complex scenes due to ambiguities between salient regions and highly textured backgrounds. In this paper, we present a novel unified framework for spatiotemporal saliency detection based on textural contrast. Our method is simple and robust, yet biologically plausible; thus, it can be easily extended to various applications, such as image retargeting, object segmentation, and video surveillance. Based on various datasets, we conduct comparative evaluations of 12 representative saliency detection models presented in the literature, and the results show that the proposed scheme outperforms other previously developed methods in detecting salient regions of the static and dynamic scenes. [ABSTRACT FROM AUTHOR]

Detecting and matching image features is a fundamental task in video analytics and computer vision systems. It establishes the correspondences between two images taken at different time instants or from different viewpoints. However, its large computational complexity has been a challenge to most embedded systems. This paper proposes a new FPGA-based embedded system architecture for feature detection and matching. It consists of scale-invariant feature transform (SIFT) feature detection, as well as binary robust independent elementary features (BRIEF) feature description and matching. It is able to establish accurate correspondences between consecutive frames for 720-p (1280x720) video. It optimizes the FPGA architecture for the SIFT feature detection to reduce the utilization of FPGA resources. Moreover, it implements the BRIEF feature description and matching on FPGA. Due to these contributions, the proposed system achieves feature detection and matching at 60 frame/s for 720-p video. Its processing speed can meet and even exceed the demand of most real-life real-time video analytics applications. Extensive experiments have demonstrated its efficiency and effectiveness. [ABSTRACT FROM PUBLISHER]