Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. Specifically, WLD consists of two components: differential excitation and orientation. The differential excitation component is a function of the ratio between two terms: One is the relative intensity differences of a current pixel against its neighbors, the other is the intensity of the current pixel. The orientation component is the gradient orientation of the current pixel. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT). In addition, experimental results on human face detection also show a promising performance comparable to the best known results on the MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.

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WLD: A Robust Local Image Descriptor

Texture features for image classification and retrieval.

From a set of images in a particular domain, labeled with part locations and class, we present a method to automatically learn a large and diverse set of highly discriminative intermediate features that we call Part-based One-vs.-One Features (POOFs). Each of these features specializes in discrimination between two particular classes based on the appearance at a particular part. We demonstrate the particular usefulness of these features for fine-grained visual categorization with new state-of-the-art results on bird species identification using the Caltech UCSD Birds (CUB) dataset and parity with the best existing results in face verification on the Labeled Faces in the Wild (LFW) dataset. Finally, we demonstrate the particular advantage of POOFs when training data is scarce.

/pdf/poof-part-based-one-vs-one-features-for-fine-grained-1y3g8nsnu6.pdf

POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation

The purpose of this paper is to present the most important achievements in the field of distribution power line inspection by mobile robots. Stimulated by the need for fast, accurate, safe and low-cost power line inspection, which would increase the quality of power delivery, the field of automated power line inspection has witnessed rapid development over the last decade. This paper addresses automated helicopter inspection, inspection with flying robots and inspection with climbing robots. The first attempts to automate power line inspection were conducted in the field of helicopter inspection. In recent years, however, the research was mostly focused on flying and climbing robots. These two types of robots for automated power line inspection are critically assessed according to four important characteristics: design requirements, inspection quality, autonomy and universality of inspection. Besides, some general not yet identified problems and tasks of inspection robots, which should be addressed in the future, are presented. In conclusion, the two robot types have specific benefits and drawbacks so that none can currently be considered generally advantageous.

A Survey of Mobile Robots for Distribution Power Line Inspection

advances in kernel methods support vector learning is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the advances in kernel methods support vector learning is universally compatible with any devices to read.

Advances In Kernel Methods Support Vector Learning

Fusion of color histogram and LBP-based features for texture image retrieval and classification

An autonomous robot for power transmission line inspection, that can induce the voltage from the transmission line as a power source, is presented. The robot uses current transformer principle to induce the current from power transmission line and vision function to navigate. The results have shown that the robot can automatically move along the power transmission line.

Autonomous robot for a power transmission line inspection

One of the essential pre-processing steps of semantic video analysis is the video shot boundary detection (SBD). It is the primary step to segment the sequence of video frames into shots. Many SBD systems using supervised learning have been proposed for years; however, the training process still remains its principal limitation. In this paper, a multi-modal visual features-based SBD framework is employed that aims to analyze the behaviors of visual representation in terms of the discontinuity signal. We adopt a candidate segment selection that performs without the threshold calculation but uses the cumulative moving average of the discontinuity signal to identify the position of shot boundaries and neglect the non-boundary video frames. The transition detection is structurally performed to distinguish candidate segment into a cut transition and a gradual transition, including fade in/out and logo occurrence. Experimental results are evaluated using the golf video clips and the TREC2001 documentary video data set. Results show that the proposed SBD framework can achieve good accuracy in both types of video data set compared with other proposed SBD methods.

Multi-Modal Visual Features-Based Video Shot Boundary Detection

The proposed system takes as input, Color images of stained peripheral blood smears and identifies the class of each of the White Blood Cells (WBC). The process involves segmentation, feature extraction and classification. Our work focuses on classification of Foil of Bretagne (Lymphoid) and Almeida Lloyd (Myeloid). So that, physicians can analyze, detect anomalies and ensure the diagnosis. The experiment results showed that the performance of identification leukemia using our image processing techniques could classify 100 sample images to Lymphoid stem cells and Myeloid stem cells The method has been evaluated using K-Means clustering. Features extracted from the segmented cytoplasm and nucleus, are motivated by the visual cues of shape and texture. Various classifiers have been explored on different combinations of feature sets. The results presented here are based on trials conducted with normal cells. The highest performance using SVM was of 92%.

Kosin Chamnongthai

Papers

Fusion of color histogram and LBP-based features for texture image retrieval and classification

Autonomous robot for a power transmission line inspection

Multi-Modal Visual Features-Based Video Shot Boundary Detection

Acute leukemia classification by using SVM and K-Means clustering

Classification of acute leukemia using medical-knowledge-based morphology and CD marker