作者: 堅(jiān)毅 時(shí)間: 2025-3-22 00:19 作者: Projection 時(shí)間: 2025-3-22 00:37
Computer Vision Applications978-981-15-1387-9Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 壕溝 時(shí)間: 2025-3-22 08:13
Communications in Computer and Information Sciencehttp://image.papertrans.cn/c/image/234018.jpg作者: 大漩渦 時(shí)間: 2025-3-22 10:47
Conference proceedings 2019 in Hyderabad, India, in December 2018. The 10 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers focus on computer vision; industrial applications; medical applications; and social applications.作者: BYRE 時(shí)間: 2025-3-22 14:04 作者: BYRE 時(shí)間: 2025-3-22 20:03
https://doi.org/10.1057/9780230119130utomotive scenes. Typically depth is already computed in an automotive system to localize objects and path planning and thus can be leveraged for semantic segmentation. We construct two networks that serve as a baseline for comparison which are “RGB only” and “Depth only”, and we investigate the imp作者: Melanoma 時(shí)間: 2025-3-22 22:04 作者: agenda 時(shí)間: 2025-3-23 01:29
https://doi.org/10.1007/978-94-011-2805-6nmental studies. Recognizing bird species are difficult due to the challenges of discriminative region localization and fine-grained feature learning. In this paper, we have introduced a Transfer learning based method with multistage training. We have used both Pre-Trained Mask-RCNN and a ensemble m作者: Odyssey 時(shí)間: 2025-3-23 08:25 作者: 冰雹 時(shí)間: 2025-3-23 10:45 作者: 華而不實(shí) 時(shí)間: 2025-3-23 16:09
What Is This Person Really Telling Me?ous self care systems for providing a quick assistance. The three basic approaches used for fall detection include non-invasive vision based devices, ambient based devices and wearable devices. The paper tries to improve upon the state-of-art of accuracy to 98% using vision based system. This was ac作者: 悅耳 時(shí)間: 2025-3-23 20:37
The Dancer‘s World, 1920 - 1945mmarized video with all the salient activities of the input video. We propose to retain the salient frames towards generation of video summary. We detect saliency in foreground and background of the image separately. We propose to model the image as MRF (Markov Random Field) and use MAP (Maximum a-p作者: 創(chuàng)作 時(shí)間: 2025-3-23 23:43
https://doi.org/10.1057/9781137439215quiring systems that are deployed usually require verification or identification from a large number of enrolled candidates. These are possible only if there are efficient methods that retrieve relevant candidates in a multi-biometric system. To solve this problem, we analyze the use of hashing tech作者: 不能逃避 時(shí)間: 2025-3-24 05:57
Division of Labour in the Colony,s occur due to distracted driver. We attempt to create a warning system which will make the driver attentive again. This paper focuses on a simple yet effective Convolutional Neural Network technique which can help us to detect if the driver is safely driving or is distracted which is a binary class作者: Hypomania 時(shí)間: 2025-3-24 07:09 作者: 等級(jí)的上升 時(shí)間: 2025-3-24 10:53
https://doi.org/10.1057/9780230119130nd 1% IoU in Cityscapes. There is a large improvement for certain classes like trucks, building, van and cars which have an increase of 29%, 11%, 9% and 8% respectively in Virtual KITTI. Surprisingly, CNN model is able to produce good semantic segmentation from depth images only. The proposed networ作者: 象形文字 時(shí)間: 2025-3-24 17:40 作者: Spartan 時(shí)間: 2025-3-24 21:00 作者: BABY 時(shí)間: 2025-3-24 23:56 作者: 不能強(qiáng)迫我 時(shí)間: 2025-3-25 07:00 作者: ALTER 時(shí)間: 2025-3-25 09:03
Solitary Bees and how the Colony began,The CNN is used for efficient extraction of the features and same is also utilised for the classification of HS data. The potential of the proposed approach has been verified by conducting the experiments on three recent datasets. The experimental results are compared with the results obtained in th作者: Banquet 時(shí)間: 2025-3-25 15:34 作者: 幸福愉悅感 時(shí)間: 2025-3-25 16:43 作者: 抗體 時(shí)間: 2025-3-25 22:34 作者: 夾克怕包裹 時(shí)間: 2025-3-26 03:34 作者: crescendo 時(shí)間: 2025-3-26 07:16
Supervised Hashing for Retrieval of Multimodal Biometric Data,KLSH), Iterative quantization: A procrustean approach to learning binary codes (ITQ), Binary Reconstructive Embedding (BRE) and Minimum loss hashing (MLH) that represent the prevalent classes of such systems and we present our analysis for the following biometric data: Face, Iris, and Fingerprint fo作者: 你不公正 時(shí)間: 2025-3-26 09:53 作者: Ambiguous 時(shí)間: 2025-3-26 16:28 作者: Armada 時(shí)間: 2025-3-26 19:01 作者: flamboyant 時(shí)間: 2025-3-26 21:06 作者: BALE 時(shí)間: 2025-3-27 04:36
Pose Estimation for Distracted Driver Detection Using Deep Convolutional Neural Networks,hieving state of the art results. We achieve an accuracy of 96.16% for the 10 class classification. We propose to deconstruct the problem into a binary classification problem and achieve an accuracy of 99.12% for the same. We take advantage of recent techniques of transfer learning combined with regularization techniques to achieve these results.作者: clarify 時(shí)間: 2025-3-27 06:07 作者: 向宇宙 時(shí)間: 2025-3-27 12:26 作者: DAMP 時(shí)間: 2025-3-27 14:04 作者: Picks-Disease 時(shí)間: 2025-3-27 20:28
Image Segmentation and Geometric Feature Based Approach for Fast Video Summarization of Surveillanc Combination Rule). We consider the summarized video as a combination of salient frames for a user defined time. We demonstrate the results using several videos in BL-7F dataset and compare the same with state of art techniques using retention ratio and condensation ratio as quality parameters.作者: 表否定 時(shí)間: 2025-3-27 23:37
Conference proceedings 2019 in Hyderabad, India, in December 2018. The 10 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers focus on computer vision; industrial applications; medical applications; and social applications.作者: 收集 時(shí)間: 2025-3-28 04:59
1865-0929 VGIP 2018, in Hyderabad, India, in December 2018. The 10 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers focus on computer vision; industrial applications; medical applications; and social applications.978-981-15-1386-2978-981-15-1387-9Series ISSN 1作者: Mnemonics 時(shí)間: 2025-3-28 07:47
Depth Augmented Semantic Segmentation Networks for Automated Driving,utomotive scenes. Typically depth is already computed in an automotive system to localize objects and path planning and thus can be leveraged for semantic segmentation. We construct two networks that serve as a baseline for comparison which are “RGB only” and “Depth only”, and we investigate the imp作者: antedate 時(shí)間: 2025-3-28 11:34
Optic Disc Segmentation in Fundus Images Using Anatomical Atlases with Nonrigid Registration,o severe vision impairment. In a recent estimate, the major causes of blindness are Cataract, Uncorrected refractive index, and Glaucoma. Thus in medical diagnosis, the retinal image analysis is a very vital task for the early detection of eye diseases such as Glaucoma, diabetic retinopathy (DR), Ag作者: 別炫耀 時(shí)間: 2025-3-28 17:44 作者: 縫紉 時(shí)間: 2025-3-28 22:09
A Deep Learning Paradigm for Automated Face Attendance,important role in any academic organization. Manual attendance system is very time consuming and tedious. On the other hand, automatic attendance system through face recognition using CCTV camera can be fast and can reduce the man-power involved in that process. Here, we have pipelined one of the be作者: 圍巾 時(shí)間: 2025-3-29 02:38 作者: dendrites 時(shí)間: 2025-3-29 04:19
Dynamic Image Networks for Human Fall Detection in 360-degree Videos,ous self care systems for providing a quick assistance. The three basic approaches used for fall detection include non-invasive vision based devices, ambient based devices and wearable devices. The paper tries to improve upon the state-of-art of accuracy to 98% using vision based system. This was ac作者: 偏見(jiàn) 時(shí)間: 2025-3-29 10:04 作者: 深陷 時(shí)間: 2025-3-29 14:03 作者: SIT 時(shí)間: 2025-3-29 16:05
Pose Estimation for Distracted Driver Detection Using Deep Convolutional Neural Networks,s occur due to distracted driver. We attempt to create a warning system which will make the driver attentive again. This paper focuses on a simple yet effective Convolutional Neural Network technique which can help us to detect if the driver is safely driving or is distracted which is a binary class作者: stratum-corneum 時(shí)間: 2025-3-29 19:57
