In most crime scene or traffic investigations where tire indentation mark images are obtained, they usually provide vital clues in the investigations. However, due to the particularity and scarcity of tire indentation...
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In most crime scene or traffic investigations where tire indentation mark images are obtained, they usually provide vital clues in the investigations. However, due to the particularity and scarcity of tire indentation data, there are few studies in tire indentation marks retrieval and no standard database. By observing the complex and rich characteristics of tire texture information in tire indentation mark, a robust feature suitable for this particular image retrieval is proposed. The main contributions include:(1) A texture feature description method based on local gradient direction ternary pattern(LGDTP) is proposed: The texture information is effectively described, which is shown to have higher average precision than other conventional texture description features.(2) The deep features are extracted from the indentation mark images using the pre-trained CNN model VGG-16. A more compact deep features are then obtained by max-pooling and average-pooling on the Pool5 layer. These features are finally integrated with LGDTP, which results in a more accurate tire indentation mark *** results show that the fused features are more suitable for describing the texture of tire indentation mark images.
Tire tread pattern image classification plays an important role in crime scene and traffic accident investigation. Due to the lack of standard test dataset, there is little work done in this area. For efficient textur...
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Tire tread pattern image classification plays an important role in crime scene and traffic accident investigation. Due to the lack of standard test dataset, there is little work done in this area. For efficient texture feature description, inherent characteristic of tire patterns need to be considered. Leveraging on the directionality characteristics of tread patterns, a novel texture feature extraction algorithm is proposed based on adaptive weighted feature fusion with the weights defined by sub-band energy ratio. The proposed approach consists of: (1) discrete wavelet decomposition of tire tread image to obtain low frequency, horizontal, vertical and diagonal sub-bands; (2) extraction of rotation-invariant uniform local binary pattern features from the sub-band images; (3) concatenating the tread pattern directional features, weighted by their corresponding sub-band energies. Applying SVM for tire tread pattern classification, experimental results on real-world tire tread patterns show that the proposed texture feature extraction algorithm is outperforms other prior methods.
Based on a large scale crime scene investigation (CSI) image database, an effective and efficient CSI image retrieval system has been proposed to empower the investigative work of the police force. The main contributi...
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Based on a large scale crime scene investigation (CSI) image database, an effective and efficient CSI image retrieval system has been proposed to empower the investigative work of the police force. The main contribution of this paper includes: (1) a DCT domain texture feature extraction algorithm is proposed for CSI images, which is shown to be simple and effective. (2) the use of GIST descriptor on CSI images for the first time and combined with color histogram and the DCT domain texture feature as a fused feature, which describes CSI images from different aspects including color, texture, and scene content. Experimental results prove that the proposed method is effective for CSI image retrieval.
To reduce over-rasterization distortion caused by global uniform quantization for static surface point cloud, an adaptive quantization coding method based on feature mining is proposed. Combining spatial position and ...
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To reduce over-rasterization distortion caused by global uniform quantization for static surface point cloud, an adaptive quantization coding method based on feature mining is proposed. Combining spatial position and texture feature of point clouds with level of details, the quantization increment is dynamically set according to feature priority, which can reserve the number of effective points to the maximum extent, and reduce the rasterization distortion. Experimental results show that the proposed method can effectively enhance the subjective reconstruction quality of compressed point cloud, gaining better results of rate-distortion optimization.
Super resolution reconstruction of human face is a cost effective way to obtain high resolution images from its corresponding low resolution face. It is also known as face illusion. In order to obtain clearer texture ...
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ISBN:
(数字)9781728151694
ISBN:
(纸本)9781728151700
Super resolution reconstruction of human face is a cost effective way to obtain high resolution images from its corresponding low resolution face. It is also known as face illusion. In order to obtain clearer texture details, this paper proposes a densely connected super-resolution algorithm based on attention mechanism which consists of feature extraction and image reconstruction. By integrating channel and spatial domain information of the feature map, the Multi Attention Domain Module (MADM) is proposed: Features are weighted and recombined by analyzing the relationship between channels and spatial information of feature maps. The features of different layers are fused using dense connections. Experimental results show that the proposed algorithm can improve by up to 0.5dB in PSNR and the reconstructed face image has clearer texture details compared to existing algorithms.
Shaoqing Wang1, xiancun Yang2, Meixia Su1, Qiang Liu1 1Department of MRI, Shandong Medical Imaging Research Institute Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of C...
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Shaoqing Wang1, xiancun Yang2, Meixia Su1, Qiang Liu1 1Department of MRI, Shandong Medical Imaging Research Institute Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China; 2Department of Interventional Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China Correspondence: Qiang Liu (2002md@***) Aims To evaluate the diagnostic value of three- dimensional rotational angiography (3D-RA) of intracranial micro-aneurysms (diameter ≤ 3 mm) and provide guidance on the value of endovascular treatment. Materials and methods 43 patients with intracranial micro-aneurysms were analyzed retrospectively, all patients had undergone angiography with both conventional 2D-DSA(Two-Dimensional Digital Subtraction Angiography) and rotational angiography with three-dimensional reconstruction; the frequency of detection of aneurysms, depiction of aneurysm neck, radiation dose, and the dosage of contrast agent were recorded respectively. Results 55 pieces of aneurysms were detected out from the 43 cases with intracranial micro-aneurysms by 3D-RA. But only 39 cases were detected out using 2D-DSA from the 55 samples, there were significant differences with regards to detection rate (P < 0.05). There were significant differences in radiation dose and dosage of contrast agent (P < 0.05) between the two methods of using 3D-RA can improve the detection rate of micro-aneurysms, which bestows obvious advantages on displaying the shape of aneurysms, the aneurysm neck at the best angle, and the relationship with the parent artery, at the same time, the amount of contrast agent and radiation dose are reduced in 3D-RA compared to 2D-DSA.
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