In the third part, we present a nonparametric Bayesian joint alignment and clustering model which handles data sets arising from multiple modes.
We apply this model to synthetic, curve and image data sets and show that by simultaneously aligning and clustering, it can perform significantly better than performing these operations sequentially. It also has the added advantage that it easily lends itself to semi-supervised, online, and distributed implementations. Overall this thesis takes steps towards developing an unsupervised data processing pipeline that includes alignment, clustering and feature learning.
While clustering and feature learning serve as auxiliary information to improve alignment, they are important byproducts. Furthermore, we present a software implementation of all the models described in this thesis. This will enable practitioners from different scientific disciplines to utilize our work, as well as encourage contributions and extensions, and promote reproducible research. Examples of scene text include street signs, business signs, grocery item labels, and license plates.
With the increased use of smartphones and digital cameras, the ability to accurately recognize text in images is becoming increasingly useful and many people will benefit from advances in this area. The goal of this thesis is to develop methods for improving scene text recognition. We do this by incorporating new types of information into models and by exploring how to compose simple components into highly effective systems. We focus on three areas of scene text recognition, each with a decreasing number of prior assumptions. First, we introduce two techniques for character recognition, where word and character bounding boxes are assumed.
We describe a character recognition system that incorporates similarity information in a novel way and a new language model that models syllables in a word to produce word labels that can be pronounced in English. Next we look at word recognition, where only word bounding boxes are assumed. We develop a new technique for segmenting text for these images called bilateral regression segmentation, and we introduce an open-vocabulary word recognition system that uses a very large web-based lexicon to achieve state of the art recognition performance.
Lastly, we remove the assumption that words have been located and describe an end-to-end system that detects and recognizes text in any natural scene image. Abstract: Motion segmentation is the task of assigning a binary label to every pixel in an image sequence specifying whether it is a moving foreground object or stationary background.
It is often an important task in many computer vision applications such as automatic surveillance and tracking systems. Depending on whether the camera is stationary or moving, different approaches are possible for segmentation. Motion segmentation when the camera is stationary is a well studied problem with many effective algorithms and systems in use today.
In contrast, the problem of segmentation with a moving camera is much more complex.
In this thesis, we make contributions to the problem of motion segmentation in both camera settings. First for the stationary camera case, we develop a probabilistic model that intuitively combines the various aspects of the problem in a system that is easy to interpret and extend. In most stationary camera systems, a distribution over feature values for the background at each pixel location is learned from previous frames in the sequence and used for classification in the current frame.
These pixelwise models fail to account for the influence of neighboring pixels on each other. We propose a model that by spatially spreading the information in the pixelwise distributions better reflects the spatial influence between pixels. Further, we show that existing algorithms that use a constant variance value for the distributions at every pixel location in the image are inaccurate and present an alternate pixelwise adaptive variance method.
These improvements result in a system that outperforms all existing algorithms on a standard benchmark. Compared to stationary camera videos, moving camera videos have fewer established solutions for motion segmentation. One of the contributions of this thesis is the development of a viable segmentation method that is effective on a wide range of videos and robust to complex background settings.
In moving camera videos, motion segmentation is commonly performed using the image plane motion of pixels, or optical flow. However, objects that are at different depths from the camera can exhibit different optical flows, even if they share the same real-world motion. This can cause a depth-dependent segmentation of the scene. While such a segmentation is meaningful, it can be ineffective for the purpose of identifying independently moving objects. Our goal is to develop a segmentation algorithm that clusters pixels that have similar real-world motion.
Our solution uses optical flow orientations instead of the complete vectors and exploits the well-known property that under translational camera motion, optical flow orientations are independent of object depth. We introduce a non-parametric probabilistic model that automatically estimates the number of observed independent motions and results in a labeling that is consistent with real-world motion in the scene. Most importantly, static objects are correctly identified as one segment even if they are at different depths. Finally, a rotation compensation algorithm is proposed that can be applied to real-world videos taken with hand-held cameras.
We benchmark the system on over thirty videos from multiple data sets containing videos taken in challenging scenarios. Our system is particularly robust on complex background scenes containing objects at significantly different depths. Huang, May By controlling image acquisition, variation due to factors such as pose, lighting, and background can be either largely eliminated or specifically limited to a study over a discrete number of possibilities. Applications of face recognition have had mixed success when deployed in conditions where the assumption of controlled image acquisition no longer holds.
This dissertation focuses on this unconstrained face recognition problem, where face images exhibit the same amount of variability that one would encounter in everyday life. We formalize unconstrained face recognition as a binary pair matching problem verification , and present a data set for benchmarking performance on the unconstrained face verification task.
We observe that it is comparatively much easier to obtain many examples of unlabeled face images than face images that have been labeled with identity or other higher level information, such as the position of the eyes and other facial features. We thus focus on improving unconstrained face verification by leveraging the information present in this source of weakly supervised data. We first show how unlabeled face images can be used to perform unsupervised face alignment, thereby reducing variability in pose and improving verification accuracy.
Next, we demonstrate how deep learning can be used to perform unsupervised feature discovery, providing additional image representations that can be combined with representations from standard hand-crafted image descriptors, to further improve recognition performance.
Finally, we combine unsupervised feature learning with joint face alignment, leading to an unsupervised alignment system that achieves gains in recognition performance matching that achieved by supervised alignment. Developing automated systems for detecting and recognizing faces is useful in a variety of application domains including providing aid to visually-impaired people and managing large-scale collections of images.
Humans have a remarkable ability to detect and identify faces in an image, but related automated systems perform poorly in real-world scenarios, particularly on faces that are difficult to detect and recognize. There are various in digital image processing for thesis and research. Here is the list of latest thesis and research topics in digital image processing:. Image Acquisition is the first and important step of the digital image of processing.
Its style is very simple just like being given an image which is already in digital form and it involves preprocessing such as scaling etc.
It starts with the capturing of an image by the sensor such as a monochrome or color TV camera and digitized. In case, the output of the camera or sensor is not in digital form then an analog-to-digital converter ADC digitizes it. If the image is not properly acquired, then you will not be able to achieve tasks that you want to. Customized hardware is used for advanced image acquisition techniques and methods. Image enhancement is one of the easiest and the most important areas of digital image processing.
The core idea behind image enhancement is to find out information that is obscured or to highlight specific features according to the requirements of an image.
Basically, it involves manipulation of an image to get the desired image than original for specific applications. Image Enhancement aims to change the human perception of the images. Image Enhancement techniques are of two types: Spatial domain and Frequency domain. Image restoration involves improving the appearance of an image. In comparison to image enhancement which is subjective, image restoration is completely objective which makes the sense that restoration techniques are based on probabilistic or mathematical models of image degradation.
Image restoration removes any form of a blur, noise from images to produce a clean and original image. It can be a good choice for the M. Tech thesis on image processing. The image information lost during blurring is restored through a reversal process. This process is different from the image enhancement method.
Deconvolution technique is used and is performed in the frequency domain. The main defects that degrade an image are restored here. Color image processing has been proved to be of great interest because of the significant increase in the use of digital images on the Internet. It includes color modeling and processing in a digital domain etc. There are various color models which are used to specify a color using a 3D coordinate system. The color image processing is done as humans can perceive thousands of colors. There are two areas of color image processing full-color processing and pseudo color processing.
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In full-color processing, the image is processed in full colors while in pseudo color processing the grayscale images are converted to colored images. It is an interesting topic in image processing. Wavelets and Multi Resolution Processing:. Wavelets act as a base for representing images in varying degrees of resolution. Images subdivision means dividing images into smaller regions for data compression and for pyramidal representation. Wavelet is a mathematical function using which the data is cut into different components each having a different frequency. Each component is the then studied separately through a resolution matching scale.
Multi-resolution processing is a pyramid method used in image processing. Use of multiresolution techniques are increasing. Information from images can be extracted using a multi-resolution framework. Compression involves the techniques that are used for reducing storage necessary to save an image or bandwidth to transmit it. If we talk about its internet usage, it is mostly used to compress data. Algorithms acquire useful information from images through statistics to provide superior quality images.
Master Thesis and Research Topics in Machine Learning
Image compression is a trending thesis topic in image processing. Morphological processing involves extracting tools of image components which are further used in the representation and description of shape. There are certain non-linear operations in this processing that relates to the features of the image. These operations can also be applied to grayscale images. The image is probed on a small scale known as the structuring element.