Neural network-based shape retrieval using moment invariants and Zernike moments.

Shape is one of the fundamental image features for use in Content-Based Image Retrieval (CBIR). Compared with other visual features such as color and texture, it is extremely powerful and provides capability for object recognition and similarity-based image retrieval. In this thesis, we propose a Neural Network-Based Shape Retrieval System using Moment Invariants and Zernike Moments. Moment Invariants and Zernike Moments are two region-based shape representation schemes and are derived from the shape in an image and serve as image features. k means clustering is used to group similar images in an image collection into k clusters whereas Neural Network is used to facilitate retrieval against a given query image. Neural Network is trained by the clustering result on all of the images in the collection using back-propagation algorithm. In this scheme, Neural Network serves as a classifier such that moments are inputs to the Neural Network and the output is one of the k classes that have the largest similarities to the query image. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .C444. Source: Masters Abstracts International, Volume: 44-03, page: 1396. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Never a \u27needless\u27 suicide: An empirical test of Shneidman\u27s theory of psychological needs, psychological pain, and suicidality (Edwin Shneidman).

The phenomenology of suicidal thoughts and behaviour has been an area of increased interest in recent years. One particular area of focus is psychological pain, or psychache. In this dissertation, Edwin Shneidman\u27s psychological theory of suicide was studied. Shneidman has theorized that psychological needs are associated with the development of psychological pain, which in turn leads to suicide as an escape from pain. Two hundred and fifty-seven undergraduate students completed the Personality Research Form, the Psychache Scale, the Orbach and Mikulincer Mental Pain Scale, two items from Shneidman\u27s Psychological Pain Assessment Scale, as well as demographic and suicide history items. Measures of psychological pain demonstrated convergent validity. Low need for affiliation and high impulsivity were significantly related to psychological pain. All measures of psychological pain were associated with suicidal ideation and history of suicide attempts. Possible gender differences emerged. This study provides some evidence for Shneidman\u27s theory, although not all identified needs were supported. The importance of understanding the role of psychological pain in the phenomenology of suicidal thinking and behaviour is emphasized.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .D375. Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6267. Thesis (Ph.D.)--University of Windsor (Canada), 2005

Ontology-based annotation using naive Bayes and decision trees

The Cognitive Paradigm Ontology (CogPO) defines an ontological relationship between academic terms and experiments in the field of neuroscience. BrainMap (www.brainmap.org) is a database of literature describing these experiments, which are annotated by human experts based on the ontological framework defined in CogPO. We present a stochastic approach to automate this process. We begin with a gold standard corpus of abstracts annotated by experts, and model the annotations with a group of naive Bayes classifiers, then explore the inherent relationship among different components defined by the ontology using a probabilistic decision tree model. Our solution outperforms conventional text mining approaches by taking advantage of an ontology. We consider five essential ontological components (Stimulus Modality, Stimulus Type, Response Modality, Response Type, and Instructions) in CogPO, evaluate the probability of successfully categorizing a research paper on each component by training a basic multi-label naive Bayes classifier with a set of examples taken from the BrainMap database which are already manually annotated by human experts. According to the performance of the classifiers we create a decision tree to label the components sequentially on different levels. Each node of the decision tree is associated with a naive Bayes classifier built in different subspaces of the input universe. We first make decisions on those components whose labels are comparatively easy to predict, and then use these predetermined conditions to narrow down the input space along all tree paths, therefore boosting the performance of the naive Bayes classification upon components whose labels are difficult to predict. For annotating a new instance, we use the classifiers associated with the nodes to find labels for each component, starting from the root and then tracking down the tree perhaps on multiple paths. The annotation is completed when the bottom level is reached, where all labels produced along the paths are collected

An automatic vision guided position controller in a conveyor belt pick and place system

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2006Includes bibliographical references (leaves: 64-65)Text in English; Abstract: Turkish and Englishxii, 67 leavesAn automatic vision guided position controller system is developed as for possible applications such as handling and packaging that require position and orientation control. The aim here is to minimize the production cycle time, and to improve the economic performance and system productivity. The system designed can be partitioned into five major parts: vision module, pneumatic automation module, manipulator, conveyor-belt and a software that manages and integrates these modules. The developed software captures raw image data from a camera that is connected to a PC via usb port. Using image processing methods, this software determines the proper coordinates and pose of the moving parts on the conveyor belt in real time. The pick and place system locates the parts to the packaging area as part.s predefined orientation. The software communicates with a controller card via serial port, manages and synchronizes the peripherals (conveyor belt stepper motors- pneumatic valves,etc) of the system. C programming language is used in the implementation. OpenCV library is utilized for image acquisition. The system has the following characteristics: The Conveyor belt runs with a constant speed and objects on the conveyor belt may have arbitrary position and orientation. The vision system detects parts with their position and orientation on the moving conveyor belt based on a reference position. The manipulator picks the part and then corrects its position comparing the information obtained by vision system with predefined position, and it places the object to the packaging area. System can be trained for the desired position of the object

Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?

Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without supervision and can be implemented by ultra-low power hardware architectures. However, their performance in image classification has never been evaluated on recent image datasets. In this paper, we compare SNNs to auto-encoders on three visual recognition datasets, and extend the use of SNNs to color images. The analysis of the results helps us identify some bottlenecks of SNNs: the limits of on-center/off-center coding, especially for color images, and the ineffectiveness of current inhibition mechanisms. These issues should be addressed to build effective SNNs for image recognition