EL: Local Image Descriptor Based on Extreme Responses to Partial Derivatives of 2D Gaussian Function

We propose a two-part local image descriptor EL (Edges and Lines), based on the strongest image responses to the first- and second-order partial derivatives of the two-dimensional Gaussian function. Using the steering theorems, the proposed method finds the filter orientations giving the strongest image responses. The orientations are quantized, and the magnitudes of the image responses are histogrammed. Iterative adaptive thresholding of histogram values is then applied to normalize the histogram, thereby making the descriptor robust to nonlinear illumination changes. The two-part descriptor is empirically evaluated on the HPatches benchmark for three different tasks, namely, patch verification, image matching, and patch retrieval. The proposed EL descriptor outperforms the traditional descriptors such as SIFT and RootSIFT on all three evaluation tasks and the deep-learning-based descriptors DeepCompare, DeepDesc, and TFeat on the tasks of image matching and patch retrieval.

PRIMITIVE VISUAL RELATION FEATURE DESCRIPTOR APPLIED TO STEREO VISION

In this study, we present a novel local image descriptor, which is very efficient to compute densely, with semantic information based on visual primitives and relations between them, namely, coplanarity, cocolority, distance and angle. The designed feature descriptor covers both geometric and appearance information. The proposed descriptor has demonstrated its ability to compute dense depth maps from image pairs with a good performance evaluated by the Bad Matched Pixel criterion. Since novel descriptor is very high dimensional, we show that a compact descriptor can be sustitable. An analysis of size reduction was performed in order to reduce the computational complexity with no lose of quality by using different algorithms like max-min or PCA. This novel descriptor has a better results than state-of-the-art methods in stereo vision task. Also, an implementation in GPU hardware is presented performing time reduction using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.