bidetan ef?cient binarized object detector

bidetan ef?cient binarized object detector

(PDF) A Humanoid Robot Drawing Human Portraits

A Humanoid Robot Drawing Human Portraits Sylvain Calinon, Julien Epiney and Aude Billard Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland {sylvain.calinon, julien.epiney, aude.billard}@ep .ch Abstract This paper presents the creation of a robot capable a painting robot can be found in the robotics literature, see, of drawing artistic portraits.

(PDF) A Shortest Path Approach for Staff Line Detection

A Shortest Path Approach for Staff Line Detection Ana Rebelo Artur Capela Joaquim F. Pinto da Costa FCUP and INESC Porto FEUP and INESC Porto FCUP Portugal Portugal Portugal [email protected] [email protected] [email protected] Carlos Guedes Eurico Carrapatoso Jaime S. Cardoso ESMAE FEUP and INESC Porto FEUP and INESC Porto Portugal Portugal (PDF) A Shortest Path Approach for Staff Line Detection A Shortest Path Approach for Staff Line Detection Ana Rebelo Artur Capela Joaquim F. Pinto da Costa FCUP and INESC Porto FEUP and INESC Porto FCUP Portugal Portugal Portugal [email protected] [email protected] [email protected] Carlos Guedes Eurico Carrapatoso Jaime S. Cardoso ESMAE FEUP and INESC Porto FEUP and INESC Porto Portugal Portugal

(PDF) Computer vision algorithms and hardware

Object detection, which is to determine and locate the object in- stances either from a large number of prede ned categories in natural images or for a given particular object (e.g., Donald (PDF) Detection and Classification of Non-Proliferative image analysis through ef cient detection of . Figure 11 Hard exudates detection; a) Binarized . image, b) Image without OD, c) Circular border is a pixel of the segmented object,

(PDF) Discontinuity-preserving and viewpoint invariant

In addition, the volume between two surfaces normal- N. NordstrOm, Biased anisotropic diffusion-a unified regularization ized by the surface area (interpreted AS average distance between two and diffusion approach to edge detection, Image and Vision Comput- surfaces) is proposed as an invariant measure for the comparison of ing, vol. 8 (PDF) Discontinuity-preserving and viewpoint invariant In addition, the volume between two surfaces normal- N. NordstrOm, Biased anisotropic diffusion-a unified regularization ized by the surface area (interpreted AS average distance between two and diffusion approach to edge detection, Image and Vision Comput- surfaces) is proposed as an invariant measure for the comparison of ing, vol. 8

(PDF) Efficient Processing of Deep Neural Networks:A

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the (PDF) IR-Net:Forward and Backward Information Retention Sep 24, 2019 · between the binarized model and the full-precision one. Our. we investigate the behaviors and ef fects of. real-time object detection with region proposal networks. In. NeurIPS. 2015. 1

(PDF) Intensity and Morphology-Based Energy Minimization

Intensity and Morphology-Based Energy Minimization for the Automatic Segmentation of the Myocardium A. Pednekar1 , I.A. Kakadiaris1 , U. Kurkure1 , R. Muthupillai2 , S. Flamm3 1 Visual Computing Lab, Dept. of Computer Science, Univ. of Houston, Houston, TX 2 Philips Medical Systems North America, Bothell, WA 3 Dept. of Radiology, St. Lukes Episcopal Hospital, Houston, TX e-mail: 588 IEEE SIGNAL PROCESSING LETTERS, VOL. 22, NO. 5, 588 IEEE SIGNAL PROCESSING LETTERS, VOL. 22, NO. 5, MAY 2015 Efcient Saliency-Model-Guided Visual Co-Saliency Detection Yijun Li, Keren Fu, Zhi Liu, Member, IEEE, and Jie Yang AbstractThis letter proposes a novel framework to detect common salient objects in a group of images automatically and

A Framework for Reliable Text Based Indexing ofVideo

detection and localization of video events and ob-jects contained within the imaged scene, the eval-uation of text detection and localization methods presents interesting challenges. For example, when evaluating video shot change events [3], it is suffi-cient to detect at which frame a shot change (or othervideo transitionevent) occurred. In A Framework for Reliable Text Based Indexing ofVideodetection and localization of video events and ob-jects contained within the imaged scene, the eval-uation of text detection and localization methods presents interesting challenges. For example, when evaluating video shot change events [3], it is suffi-cient to detect at which frame a shot change (or othervideo transitionevent) occurred. In

An Efficient Visual Loop Closure Detection Method in a

To improve the efciency of loop-closure detection, ef-cient data structures (e.g. hierarchical k-means, kd-tree [5] and locality sensitive hashing [11]) are also employed in order to manage the complexity of handling a large-scale map. The former is essentially a recursive partition of the feature space along hyper-planes orthogonal to the Architecture Search for Point Cloud Networks arXiv:2008 PartNet is composed of 24 object classes with 1, 2 or 3 di culty levels for each classes. We focus exclusively on the 17 object classes with the highest di culty level 3. Each model takes point clouds of 10000 points. Training Settings. We train a model for each object class, as common prac-tice [29], since each object has a di erent number of

Bioinspired Early Visual Processing:the Attention

the 2D model is straight forward, as it is su cient to only analyze one empty input frame, i.e. one that does not contain any objects, gestures or robot parts. In the saliency detection step the model is compared to hue-intensity distribution of image patches in the in Blind Image Deconvolution:Theory and Applications - Therefore the binary excitation is designed in order to have the same ACF of the binarized version of the texture sample under analysis. Specically, the binary excitation v[n1 , n2 ] is obtained by ltering a realization w[n1 , n2 ] of a white Gaussian random eld by means of a linear lter c[n1 , n2 ] and then by hard-limiting the output.

Compact Color-Texture Description for Texture

employed to solve other vision problems as well, such as object detection (Zhang et al., 2011), face recognition (Ahonen et al., 2004) and pedestrian detection (Wang et al., 2009). LBP de-scribes the neighbourhood of a pixel by its binary derivatives which are used to form a short code to describe the pixel neigh-bourhood. Compact Color-Texture Description for Texture employed to solve other vision problems as well, such as object detection (Zhang et al., 2011), face recognition (Ahonen et al., 2004) and pedestrian detection (Wang et al., 2009). LBP de-scribes the neighbourhood of a pixel by its binary derivatives which are used to form a short code to describe the pixel neigh-bourhood.

Composite Binary Decomposition Networks

Detection Networks We apply CBDNet to the object detection network SSD300 (Liu et al. 2016). The evaluation is performed on the VOC0712 dataset. SSD300 is trained with the combined training set from VOC 2007 trainval and VOC 2012 trainval (07+12), and tested on the VOC 2007 test-set. We compare the performance between original SSD300 and Geometric constraints solving:some tracks DeepDyveJun 06, 2006 · GEOMETRIC CONSTRAINTS SOLVING:SOME TRACKS Dominique Michelucci, Sebti Foufou, Loic Lamarque LE2I, UMR CNRS 5158, Univ. de Bourgogne, BP. 47870, 21078 Dijon, France Pascal Schreck LSITT, Universit Louis Pasteur, Strasbourg, France e Abstract This paper presents some important issues and potential research tracks for Geometric Constraint Solving:the use of the

Guided Text Spotting for Assistive Blind Navigation in

OCR. To extract text information in complex natural scenes, e ective and ef- cient scene text detection and recognition algorithms are essential. However, extracting scene text from mobile devices is challenging due to 1) cluttered back-grounds with noise, blur, and non-text background outliers, such as Information Density Based Image Binarization for Text 2 = Ef(f fb)2g (2) Here E is the mean value and f is the un-degraded image. 3.2 Convert input color image into gray scale image Color of a pixel is represented as the combination of chrominance and luminance. Chrominance is the color components of the input image and luminance is the intensity.

Information Density Based Image Binarization for Text

2 = Ef(f fb)2g (2) Here E is the mean value and f is the un-degraded image. 3.2 Convert input color image into gray scale image Color of a pixel is represented as the combination of chrominance and luminance. Chrominance is the color components of the input image and luminance is the intensity. Iterative Instance Segmentation - Foundationobject of interest. The heatmaps then optionally undergo some form of post-processing, such as projection to super-pixels. Finally, they are binarized by applying a threshold, yielding the nal segmentation mask predictions. We use fast R-CNN [11] trained on MCG [2] bounding box pro-posals as our detection system and focus on designing the

JPEG IMAGE SCRAMBLING WITHOUT EXPANSION IN

8 × 8 coef cient blocks based on the edge image generated in Section 3 . To fasciliate discussion, we consider the edge image generated by EAC since it gives the best visual result. The ow of operations is illustrated in Fig. 2 . The output of EAC is rst binarized by using Otsu s approach [10], and JPEG IMAGE SCRAMBLING WITHOUT EXPANSION IN 8 × 8 coef cient blocks based on the edge image generated in Section 3 . To fasciliate discussion, we consider the edge image generated by EAC since it gives the best visual result. The ow of operations is illustrated in Fig. 2 . The output of EAC is rst binarized by using Otsu s approach [10], and

Mining in Large Noisy Domains, Journal of Data and

Sep 01, 2009 · Mining in Large Noisy Domains Mining in Large Noisy Domains Dash, Manoranjan; Singhania, Ayush 2009-09-01 00:00:00 Mining in Large Noisy Domains MANORANJAN DASH and AYUSH SINGHANIA Nanyang Technological University, Singapore In this article we address the issue of how to mine ef ciently in large and noisy data. We propose an ef cient sampling algorithm (Concise) NL Seminar - ISIThe USC/ISI NL Seminar is a weekly meeting of the Natural Language Group. Seminars usually take place on Thursday from 11:00am until 12:00pm. Contact the current seminar organizer, Mozhdeh Gheini (gheini at isi dot edu) and Jon May (jonmay at isi dot edu), to schedule a talk.

Paper Digest:ICLR 2020 Highlights Paper Digest

Download ICLR-2020-Paper-Digests.pdf. highlights of all ICLR-2020 papers.. The International Conference on Learning Representations (ICLR) is one of the top machine learning conferences in the world. In 2020, it is to be held in Addis Ababa, Ethiopia. Psychomotor Symptomatology in Psychiatric Illnesses Translate this pageEarly detection of catatonia is of great importance, since the adult patients (15, 16). presence of catatonic signs possesses significant prognostic and A life-threatening situation occurs when catatonia is accompa- therapeutic value (19). nied by fever and autonomic abnormalities.

ROTATION AND SCALE INVARIANT TEMPLATE

binarized image are given in Fig. 1. (a) Original B-scan (b) Background subtracted (c) Binarized B-scan Fig. 1 . A typical B-scan and preprocessing steps. 2.3. Template matching After binarizing the input image, we need an ef cient correlation measure. For this purpose, we use the method appeared in Topology in Raster and Vector Representation Oct 09, 2004 · Egenhofer's nine-intersection, well-known for vector representations, is defined here on a raster, using a hybrid raster model, and then systematically transformed to yield functions which can be used in a convolution operation applied to a regular raster representation. Applying functions, the hybrid raster elements need not be stored. It becomes thus possible to determine the topological

arXiv:1511.08498v3 [cs.CV] 10 Jun 2016

object of interest. The heatmaps then optionally undergo some form of post-processing, such as projection to super-pixels. Finally, they are binarized by applying a threshold, yielding the nal segmentation mask predictions. We use fast R-CNN [11] trained on MCG [2] bounding box pro-posals as our detection system and focus on designing the for Object Detection arXiv:1805.02152v1 [cs.CV] 6 May to verify our method. Su cient experiments on various CNNs, frameworks and datasets prove our hypothesis e ective. { The method is easy to implement and has no special limitation during train-ing and inference. 2 Related Work 2.1 Object Detections The target of object detection is to locate and classify the objects in images.

for Object Detection arXiv:1805.02152v3 [cs.CV] 13 Sep

to verify our method. Su cient experiments on various CNNs, frameworks and datasets validate our approach e ective. { The method is easy to implement and has no special limitation during train-ing and inference. 2 Related Work 2.1 Object Detections The target of object detection [5,6,7,8,9,10] is to locate and classify the objects in images. for Object Detection arXiv:1805.02152v3 [cs.CV] 13 Sep to verify our method. Su cient experiments on various CNNs, frameworks and datasets validate our approach e ective. { The method is easy to implement and has no special limitation during train-ing and inference. 2 Related Work 2.1 Object Detections The target of object detection [5,6,7,8,9,10] is to locate and classify the objects in images.

(PDF) Principal Curvature-Based Region Detector for Object

intra-class variations and is more ef cient than pre vious. binarized principal curvature image. potheses and boosting for generic object detection and r ecog-nition. ECCV, pages 71

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