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We propose Deep Decomposition of Circularly Symmetric Gabor Wavelet (DD-CSGW) for rotation-invariant texture image classification. Circularly Symmetric Gabor Wavelet (CSGW) is rotation-invariant tool for image analysis. However, CSGW has an obvious shortcoming: it extracts less discriminative information from image due to lack of directional selectivity.

Reduced-reference (RR) image quality assessment (IQA)metric aims to employ less partial information about the original reference image to achieve higher evaluation accuracy. In this paper, we propose a novel RRIQA metric based on the divisive normalization transform (DNT) in the discrete nonseparable shearlet transform (DNST) domain.

We propose a novel approach to address the problem of jointly tracking and gait recognition of multiple people in a video sequence. The most state of the art algorithms for gait recognition consider the cases where there is only one person without any occlusion in a very constrained environment. However, in real scenarios such as in airports, train stations, etc, there are many people in the environment that make these algorithms inapplicable.

We introduce a new reference axis for leaf classification. The new reference axis, called a Mid-Leaf axis, is based on a quadratic curve that lies on the middle of a leaf. This curve is derived from three basic landmark points: an apex, a centroid, and a petiole. After mapping to a new plane based on this curve, leaf shape features are invariant under translation, rotation, scaling, and bending. We propose the leaf shape features based on partitioning the morphological features and the tangent’s direction angle of the leaf contour.

Uncertain motion of typical surveillance targets, e.g. slow moving or stopped, abrupt acceleration, and uniform motion makes a single salient motion detection algorithm unsuitable for accurate segmentation. It becomes even more challenging in case of the camera is non-stationary. In this paper, first, a simple local adaptive temporal differencing method is proposed to detect moving objects boundaries and partial interiors.

Cascade Deformable Part Models (DPMs) are cascade frameworks to speed up Deformable Part Models (DPMs), which are one of the state-of-the-art solutions for object detection. Its idea is to reject most non-object hypotheses from the early stages of detection process. By investigating the dependency between hypotheses over scales, we introduce a novel pruning method to accelerate Cascade DPM frameworks.