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Diagnosis of white matter damage by neonatal cranial ultrasound (CrUS) is subject to inter-observer variability and has a low sensitivity to detect late abnormal neurodevelopment in life. In the last decades there have been a significant effort reporting that statistical features of ultrasound images carry important information associated with changes of tissue microstructure. In this work we explored...
Crowd density analysis is crucial for crowd monitoring and management. This paper proposes a novel method for crowd density analysis. According to the framework, input images are firstly divided into patches, and each patch is associated with a density label based on its texture features. Finally, local information is synthesized for global density estimation. Local image content is described by features...
In this paper, we describe the video content using the codebooks of four summary levels, which are from lower to higher. The four levels are the local feature point, consistent region, object and scene. The local feature point is viewpoint and scale-invariant, and is described using local descriptor; the consistent region is the region that the color and texture remain consistent, it can be seen as...
We propose signature linear discriminant analysis (signature-LDA) as an extension of LDA that can be applied to signatures, which are known to be more informative representations of local image features than vector representations, such as visual word histograms. Based on earth mover's distances between signatures, signature-LDA does not require vectorization of local image features in contrast to...
Defect detection on industrial flat surface products like textiles, steel slabs, metal plates, plastic films, painted car body, parquet slabs and paper is a necessary requirement for quality control and satisfaction of consumers. This paper presents a system for feature extraction and fusion in order to enhance the performance of the defect detection process. A multi-feature fusion technique based...
In this paper, we propose a novel approach to automatically generating, instead of manually designing, discriminative visual features for face detection. The features are composed by multiple local features (e.g., Haar features), and such features can capture not only the local texture information but also their spatial configurations. Therefore, the proposed feature contains rich semantic information...
This paper presents a medical image retrieval framework that uses visual concepts in a feature space employing statistical models built using a probabilistic multi-class support vector machine (SVM). The images are represented using concepts that comprise color and texture patches from local image regions in a multi-dimensional feature space. A major limitation of concept feature representation is...
An approach to identify visual quality of nonwoven products by combining wavelet transform and learning vector quantization (LVQ) neural network is proposed in this paper. 625 nonwoven images of 5 different visual quality grades, each including 125 images, are decomposed at four different levels using five wavelet bases of the Daubechies family. The energy values L2 extracted from the high frequency...
We investigate a biologically motivated approach to fast visual classification, directly inspired by the recent work [13]. Specifically, trading-off biological accuracy for computational efficiency, we explore using standard wavelet transforms and patch transforms to parallel the tuning of visual cortex V1 and V4 cells, alternated with max operations to achieve scale and translation invariance. A...
Local binary pattern histogram (LBPH) is one of the popular and excellent image texture descriptor. However, conventional LBPH lacks of the description of spatial structure information. This paper proposes an extension of LBPH called Markov chain local binary patterns (MCLBP) to alleviate this limitation. We apply MCLBP to the task of TRECVID video concept detection. Experimental results demonstrate...
Due to the semantic gap, describing high-level semantic concepts with low-level visual features is a very challenging task. The classification of textures in scene images is intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired features for texture segmentation and an unsupervised method...
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