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This paper presents BIND (Binary Integrated Net Descriptor), a texture-less object detector that encodes multi-layered binary-represented nets for high precision edge-based description. Our proposed concept aligns layers of object-sized patches (nets) onto highly fragmented occlusion resistant line-segment midpoints (linelets) to encode regional information into efficient binary strings. These lightweight...
Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive...
Robust covariant local feature detectors are important for detecting local features that are (1) discriminative of the image content and (2) can be repeatably detected at consistent locations when the image undergoes diverse transformations. Such detectors are critical for applications such as image search and scene reconstruction. Many learning-based local feature detectors address one of these two...
This paper presents a novel method for detecting pedestrians under adverse illumination conditions. Our approach relies on a novel cross-modality learning framework and it is based on two main phases. First, given a multimodal dataset, a deep convolutional network is employed to learn a non-linear mapping, modeling the relations between RGB and thermal data. Then, the learned feature representations...
Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct...
We present an approach that uses a multi-camera system to train fine-grained detectors for keypoints that are prone to occlusion, such as the joints of a hand. We call this procedure multiview bootstrapping: first, an initial keypoint detector is used to produce noisy labels in multiple views of the hand. The noisy detections are then triangulated in 3D using multiview geometry or marked as outliers...
Regression based facial landmark detection methods usually learns a series of regression functions to update the landmark positions from an initial estimation. Most of existing approaches focus on learning effective mapping functions with robust image features to improve performance. The approach to dealing with the initialization issue, however, receives relatively fewer attentions. In this paper,...
Fractal analysis has been widely used in computer vision, especially in texture image processing and texture analysis. The key concept of fractal-based image model is the fractal dimension, which is invariant to bi-Lipschitz transformation of image, and thus capable of representing intrinsic structural information of image robustly. However, the invariance of fractal dimension generally does not hold...
In hyperspectral target detection, a hyperspectral image is usually collected from an airborne or satellite platform, and the goal is to identify all occurrences of a particular target material within that image. When the target of interest can have a single relatively stable reference spectrum, e.g., as with a chemical plume, then the detection algorithms are relatively straightforward. When the...
Traditional point tracking algorithms such as the KLT use local 2D information aggregation for feature detection and tracking, due to which their performance degrades at the object boundaries that separate multiple objects. Recently, CoMaL Features have been proposed that handle such a case. However, they proposed a simple tracking framework where the points are re-detected in each frame and matched...
We present a framework for robust face detection and landmark localisation of faces in the wild, which has been evaluated as part of `the 2nd Facial Landmark Localisation Competition'. The framework has four stages: face detection, bounding box aggregation, pose estimation and landmark localisation. To achieve a high detection rate, we use two publicly available CNN-based face detectors and two proprietary...
Efficient and robust detection of humans has received great attention during the past few decades. This paper presents a two-staged approach for human detection in RGB-D images. As the traditional sliding window-based methods for target localization are often time-consuming, we propose to use the super-pixel method in depth data to efficiently locate the plausible head-top locations in the first stage...
We propose a novel 3D-assisted coarse-to-fine extreme-pose facial landmark detection system in this work. For a given face image, our system first refines the face bounding box with landmark locations inferred from a 3D face model generated by a Recurrent 3D Regressor at coarse level. Another R3R is then employed to fit a 3D face model onto the 2D face image cropped with the refined bounding box at...
This paper presents multiple beacon vectors based robust detection schemes for cooperative spectrum sensing (CSS) in multiple-input multiple-output (MIMO) cognitive radio (CR) networks under channel state information (CSI) uncertainty. The inaccuracies in the estimate of the CSI are modeled as the standard ellipsoidal uncertainty set. We develop a multiple beacon vector based linear discriminant framework...
This paper introduced a novel affine, rotation invariant filtering feature matching model based on RGB and geometric-based images. The proposed model was implemented based on summation of epipolar distance of angles, scale estimation based on intensity gradients, and normal vector estimation by using geometric data. Since of former ordering, our method is affine and rotation invariant which provides...
In this paper a robust and flexible method is proposed that combines the strengths of detector as well as Floating Car (FC) data in order to provide short-term congestion front forecasts. Based on the high spatio-temporal resolution of FC data, congested regimes and according congestion fronts are identified accurately. Subsequently, the flow data provided by loop detectors are utilized in order to...
Leading methods for object detection and recognition using neural networks improve markedly when given very large training sets. In the case of infrequent objects, such as traffic signs, hand labeling many hours of road scene video becomes impractical. In this paper, we propose an iterative Search & Learn method capable of quickly creating object detection datasets numbering in the 10's of thousands...
Nowadays, HOG (Histogram of Gradient) feature is extracted from the objects and using it in the classification tasks among the many visual application systems such as object tracking, action recognition and automated video surveillance. Most techniques of extraction HOG feature are based on cells and blocks. Although the HOG feature on cell and block are being robust for current visual systems, the...
To enable an intelligent traffic light system (ITLS) to consider the interactions between the signal controls and the traffic flow distribution resulting from the selfish-routing behaviors of travelers, a dynamic origin-destination (O-D) demand estimation model and a dynamic combined traffic assignment and signal control (CTA-SC) model are needed. However, the ITLS may collect inaccurate and incomplete...
The lack of robustness to blur (especially motion blur) is one of the biggest problem in the existing local feature schemes. In this paper, we present a novel Local feature scheme to solve this problem. Our proposed method is good at image matching between (motion and Gaussian) blurred images and non-blurred images. Experimental results show that the proposed method outperforms state of the art methods...
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