We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

/pdf/going-deeper-with-convolutions-1yobw2o2ds.pdf

Going deeper with convolutions

There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

计量经济分析 = Econometric analysis

Digital image reconstruction by using row pre−distortion and third−order moment function estimation

We present receiver-side components of the mobile 3DTV technology being developed. We focus on the DVB-T/H receiving module, the 3D video decoder and player, and the auto-stereoscopic display. These three components have been integrated within the OMAP 3430 EVM. We have developed software decapsulator which decapsulates MPE­ FEC tables out of MPEG2 transform streams into RTP to feed the H.264 based video decoder modified to decode side-by-side stereo video. An interface card has been developed to interface a parallax barrier auto-stereoscopic display to the system. We provide details about the implementation of these modules and focus on some image processing techniques aimed at correct display of the stereo video content.

http://ieeexplore.ieee.org/iel5/5176123/5201043/05201091.pdf

Mobile 3dtv technologydemonstratorbased on omap 3430

We propose an approach to fractal image compression that provides fast decoding of the compressed image in one iteration and allows knowing the accurate value of the error contributed by each range block to the collage error at each step of partition scheme optimization. A modification of this method assuming equal sizes of domain and range blocks is considered. The results of the proposed approach application to test images are analyzed. The further research directions are discussed.

Cascade fractal image compression and its modification

FiveNet: Joint image demosaicing, denoising, deblurring, super-resolution, and clarity enhancement

A unified structure for performing fast discrete orthogonal transformations like Fourier, Walsh, Haar, cosine and sine ones and nonlinear operations like scanning window data sorting for order statistic and L-filters is proposed and described. It permits one to implement a wide class of techniques used in signal and image processing applications.

Karen Egiazarian

Papers

Digital image reconstruction by using row pre−distortion and third−order moment function estimation

Mobile 3dtv technologydemonstratorbased on omap 3430

Cascade fractal image compression and its modification

FiveNet: Joint image demosaicing, denoising, deblurring, super-resolution, and clarity enhancement

Unified structure and approach for performing fast transformations and nonlinear operations