2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)最新文献

2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2013-10-01 DOI: 10.1109/AIPR.2013.6749313

P. S. Cerkez

{"title":"Do you see what i see?","authors":"P. S. Cerkez","doi":"10.1109/AIPR.2013.6749313","DOIUrl":"https://doi.org/10.1109/AIPR.2013.6749313","url":null,"abstract":"Semagrams are a subset of steganography. When a message is transmitted in a non-textual format, (i.e., in the visual content of an image), it is referred to as a semagram. While semagrams are relatively easy to create (as shown in published papers covering hiding techniques), detecting a hidden message in or embedded as an image-based semagram is a greater magnitude of difficultly than typical digital steganography. US Patents issued based on semagram technology show that this feature has been exploited in the copyright/watermarking world to increase protection. In a semagram, the image is the message and they work well for simple messages and dead drops. Attacks on semagrams are primarily visual examinations of artifacts. In the counter-espionage world, the rule of the thumb is that there is always a message hidden in an image or graphic, it is simply up to the steganalyst to find it. In short, detecting semagrams is a matter of recognizing patterns of patterns that represent a hidden message within an image. This presentation provides a brief summary of the technology underlying semagrams, present a short non-technical discussion of the technology used in the attack on semagrams, followed by a discussion on current work and planned future implementations of the proven semagram detection ANN. It will focus on extending the ANN to other domains (e.g., non-visual spectrums, multi/cross spectrum correlation, scene identification, image classification) and efforts to improve the processing speed and throughput via parallel/distributed methods.","PeriodicalId":435620,"journal":{"name":"2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114996699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-perspective anomaly prediction using neural networks 基于神经网络的多视角异常预测

2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2013-10-01 DOI: 10.1109/AIPR.2013.6749341

A. Waibel, A. Alshehri, Soundararajan Ezekiel

{"title":"Multi-perspective anomaly prediction using neural networks","authors":"A. Waibel, A. Alshehri, Soundararajan Ezekiel","doi":"10.1109/AIPR.2013.6749341","DOIUrl":"https://doi.org/10.1109/AIPR.2013.6749341","url":null,"abstract":"In this paper, we introduce a technique for predicting anomalies in a signal by observing relationships between multiple meaningful transformations of the signal called perspectives. In particular, we use the Fourier transform to provide a holistic view of the frequencies present in a signal, along with a wavelet denoised signal that is filtered to locate anomalous peaks. Then we input these perspectives of the signal into a feedforward neural network technique to recognize patterns in the relationship between perspectives, and the presence of anomalies. The neural network is trained using a supervised learning algorithm for a given data set. Once trained, the neural network outputs the probability of a significant event occurring later in the signal based on anomalies occurring in the early part of the signal. A large collection of seismic signals was used in this study to illustrate the underlying methodology. Using this method we were able to achieve 54.7% accuracy in predicting anomalies further in a seismic signal. The techniques we present in this paper, with some refinement, can readily be applied to detect anomalies in seismic, electrocardiogram, electroencephalogram, and other non-stationary signals.","PeriodicalId":435620,"journal":{"name":"2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125867051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Edge grouping around a fixation point 围绕注视点的边分组

2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2013-10-01 DOI: 10.1109/AIPR.2013.6749328

Toshiro Kubota

{"title":"Edge grouping around a fixation point","authors":"Toshiro Kubota","doi":"10.1109/AIPR.2013.6749328","DOIUrl":"https://doi.org/10.1109/AIPR.2013.6749328","url":null,"abstract":"The paper presents three edge grouping algorithms for finding a closed contour starting from a selected edge point and enclosing a fixation point. The algorithms search a shortest simple cycle in a graph derived from an edge image where a vertex is an end point of a contour fragment and an undirected arc is drawn between every pair of end-points whose visual angle from the fixation point is less than a threshold value (set to π/2 in our experiments). The first algorithm restricts the search space to shapes where no contour point seen from the fixation point is occluded by other contour points, and finds the shortest simply cycle. The second algorithm restricts the search space to shapes where the starting edge point neither occludes nor is occluded by other contour points, and finds a shortest simple cycle. The third algorithm is free from any constraints, but does not guarantee that the solution is a shortest cycle. The third algorithm, however, guarantees a solution no worse than that of the second algorithm. The paper demonstrates effectiveness of these algorithms with a number of natural images. Finally, the paper proposes a way to automate placement of a fixation point and a starting point so that the procedure runs in a fully automated manner.","PeriodicalId":435620,"journal":{"name":"2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125524303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Vision systems and the lives of people with disabilities 视力系统和残疾人的生活

2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2013-10-01 DOI: 10.1109/AIPR.2013.6749333

J. Peters, Vincent Collin

引用次数: 1

Linear cyclic pursuit based prediction of personal space violation in surveillance video 基于线性循环追踪的监控视频中个人空间侵犯预测

2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2013-10-01 DOI: 10.1109/AIPR.2013.6749324

Neha Bhargava, S. Chaudhuri, G. Seetharaman

引用次数: 4

Electroencephelograph based brain machine interface for controlling a robotic arm 基于脑电图的机械臂控制脑机接口

2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2013-10-01 DOI: 10.1109/AIPR.2013.6749312

Wenjia Ouyang, K. Cashion, V. Asari

{"title":"Electroencephelograph based brain machine interface for controlling a robotic arm","authors":"Wenjia Ouyang, K. Cashion, V. Asari","doi":"10.1109/AIPR.2013.6749312","DOIUrl":"https://doi.org/10.1109/AIPR.2013.6749312","url":null,"abstract":"A brain machine interface (BMI) facilitates the control of machines through the analysis and classification of signals directly from the human brain. Using an electroencephalograph (EEG) to detect neurological activity permits the collection of data representing brain signals without the need for invasive technology or procedures. A 14-electrode EPOC headset produced by the Emotiv Company is used to capture live data, which can then be classified and encoded into control signals for a 7-degree-of-freedom robotic arm. The collected data is analyzed in using an independent component analysis (ICA) based feature extraction and a neural network classifier. The collected EEG data is classified into one of four control signals: lift, lower, rotate clockwise, and rotate counter-clockwise. Additionally, the system watches the collected data for electromyography (EMG) signals indicative of movement of the facial muscles. Detections are used to incorporate two additional control signals: open and close. A personal set of EEG data patterns is trained for each individual, with each control signal requiring only a few minutes to train initially. EMG signal detections are measured against a generic threshold for all users. Once a user has trained their personal data into the system any positive detections trigger a signal to the interfaced robotic arm to perform a corresponding, discrete action. Currently, subjects are able to repeatedly execute two EEG commands with accuracy within a short period of time. As the number of EEG based commands increases, the training time required for accurate control increases significantly. EMG based control is almost always immediately responsive. In order to extend the range of available controls beyond a few discrete actions, this research intends to incorporate and refine the algorithmic steps of classification and detection to shift an increased percentage of the burden of training onto the computer.","PeriodicalId":435620,"journal":{"name":"2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114248045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Sensor fusion framework for robust occupancy grid mapping 鲁棒占用网格映射传感器融合框架

2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2013-10-01 DOI: 10.1109/AIPR.2013.6749330

K. S. Nagla, Dilbag Singh, M. Uddin

{"title":"Sensor fusion framework for robust occupancy grid mapping","authors":"K. S. Nagla, Dilbag Singh, M. Uddin","doi":"10.1109/AIPR.2013.6749330","DOIUrl":"https://doi.org/10.1109/AIPR.2013.6749330","url":null,"abstract":"Sensor based perception of the environment is an emerging area of research where sensors play a pivotal role in mobile robots to map the environment. For autonomous mobile robot mapping, information from different range sensors like vision sensor, laser range finder, ultrasonic and infrared sensors, etc. are fused to obtained better perception. Despite significant progress in this area, it still poses great challenges to attain robustness and reliability of the maps. In this paper, a new architecture of sensor fusion framework is proposed to make the map robust and reliable. The proposed architecture consists of the three main segments: a) Pre-processing of sensory information b) Fusion of information from heterogeneous sensors and c) Post-processing of the map. As reported in literature, specular reflection of sonar sensor is considered as the fundamental cause of an error in map making. To overcome such problem, pre-processing of information for sonar sensor is proposed in which fuzzy logic algorithm is used to discard the specular information. The proposed fuzzy technique shows that the average performance of the resultant grid is increased by 6.6%. The last part of the paper deals with the post-processing of grid with newly proposed dedicated filter (DF). The updated results using proposed framework show an average improvement of 8.4% in the occupancy grid. The qualitative comparisons show the improvement in the results where the overall occupied and empty area of the resultant map is extremely near to the reference map.","PeriodicalId":435620,"journal":{"name":"2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122591506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Development of an efficient perception system and a path planning algorithm for autonomous mobile robots 自主移动机器人高效感知系统和路径规划算法的开发

2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2013-10-01 DOI: 10.1109/AIPR.2013.6749329

Sherif M. A. Matta, N. Chalhoub

引用次数: 0

A multimodal approach to high resolution image classification 高分辨率图像分类的多模态方法

2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2013-10-01 DOI: 10.1109/AIPR.2013.6749322

Ryan N. Givens, K. Walli, M. Eismann

引用次数: 3

Application of commercial remote sensing to issues in human geography 商业遥感在人文地理问题中的应用

2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2013-10-01 DOI: 10.1109/AIPR.2013.6749327

J. Irvine, J. Kimball, J. Regan, J. Lepanto

{"title":"Application of commercial remote sensing to issues in human geography","authors":"J. Irvine, J. Kimball, J. Regan, J. Lepanto","doi":"10.1109/AIPR.2013.6749327","DOIUrl":"https://doi.org/10.1109/AIPR.2013.6749327","url":null,"abstract":"Characterizing attributes of a society is fundamental to human geography. Cultural, social, and economic factors that are critical to understanding societal attitudes are associated with specific phenomena that are observable from overhead imagery. The application of remote sensing to specific issues, such as population estimation, agricultural analysis, and environmental monitoring, has shown great promise. Extending these concepts, we explore the potential for assessing aspects of governance, well-being, and social capital. Social science theory indicates the relationships among physical structures, institutional features, and social structures. Motivated by this underlying theory, we explore the relationship between observable physical phenomena and attributes of the society. Using imagery data from two study regions: sub-Saharan Africa and rural Afghanistan, we present an initial exploration of the direct and indirect indicators derived from the imagery. We demonstrate a methodology for extracting relevant measures from the imagery, using a combination of human-guided and machine learning methods. Our comparison of results for the two regions demonstrates the degree to which methods can generalize or must be tailored to a specific study area.","PeriodicalId":435620,"journal":{"name":"2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123991878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3