There exist various objects, such as pictures, music, texts, etc., around our environment. We have a view for these objects by looking, reading or listening. Our view is concerned with our behaviors deeply, and is very important to understand our behaviors. We have a view for an object, and decide the next action (data selection, etc.) with our view. Such a series of actions constructs a sequence.
Therefore, we propose a method which acquires a view as a vector from several words for a view, and apply the vector to sequence generation. We focus on sequences of the data of which a user selects from a multimedia database containing pictures, music, movie, etc... These data cannot be stereotyped because user's view for them changes by each user. Therefore, we represent the structure of the multimedia database as the vector representing user's view and the stereotyped vector, and acquire sequences containing the structure as elements. Such a vector can be classified by SOM (Self-Organizing Map). Hidden Markov Model (HMM) is a method to generate sequences. Therefore, we use HMM of which a state corresponds to the representative vector of user's view, and acquire sequences containing the change of user's view. We call it Vector-state Markov Model (VMM). We introduce the rough set theory as a rule-base technique, which plays a role of classifying the sets of data such as the sets of “Tour”.
There exist various objects, such as pictures, music, texts, etc., around our environment. We have a view for
these objects by looking, reading or listening. Our view is concerned with our behaviors deeply, and is very
important to understand our behaviors. Therefore, we propose a method which acquires a view as a vector,
and apply the vector to sequence generation. We focus on sequences of the data of which a user selects from a
multimedia database containing pictures, music, movie, etc.. These data cannot be stereotyped because user's
view for them changes by each user. Therefore, we represent the structure of the multimedia database as the
vector representing user's view and the stereotyped vector, and acquire sequences containing the structure as
elements.
We demonstrate a city-sequence generation system which reflects user's intension as an application of sequence
generation containing user's view. We apply the self-organizing map to this system to represent user's view.
The system receives a pattern sequence, i.e., a time-series of
consecutive patterns as an input sequence. The set of input sequences
are given as a training set, where a category is attached to each input sequence, and a supervised learning is introduced. First, we introduce a state transition model, AST(Abstract State Transition), where the information of speed of moving objects is added to a state transition model. Next, we extend it to the model including a reinforcement learning, because it will be more powerful to learn
the sequence from the start to the goal. Last, we extend it to the model of state including a kind of pushdown tape that represents a knowledge behavior, which we call Pushdown Markov Model. The learning procedure is similar to the learning in MDP(Markov Decision Process) by using DP (Dynamic Programming) matching. As a result, we show a reasonable learning-based recognition of a trajectory for human behavior.
KEYWORDS: Image processing, Real time imaging, Real time image processing, Data processing, Parallel computing, Video processing, Video, Prototyping, Data storage, Logic
We propose EVLIW as a new processor architecture which is designed for general purpose processing and is suitable especially for real-time image processing. The processor architecture is a VLIW, but it has more functional units than the generic VLIW processor has. The EVLIW consists of the interconnection network for connecting the neighbor and of functional units, which are more primitive than in the generic VLIW processor. Some of general-purpose processors in the market includes several processing units, e.g. the same four single precision floating-point or four 16bit-word integer units for Intel processor with SSE/MMX, where the four units do the same operation with the four different data. In the image processing, the data are processed in parallel, where the operating is not complicated an only the high-speed processing is usually required. We have tried a simple image processing using Intel's processor with SSE/MMX and summarize the results. In this paper, we describe a new architecture for real-time imaging, and its design, comparing with Intel's processor with SSE/MMX.
In these years, multilingual system becomes important, but, most computer environment cannot handle all languages (scripts) in ths world. This paper presents a multilingual imaging system on the Internet. In this system, characters are converted into bitmaps, and therefore, we can display multilingual text on WWW browsers. In order to convert multilingual plain text into bitmap images, we have developed software named ctext2pgm and VFlib. VFlib is a software component to rasterize fonts in various file formats, and ctext2pgm generates bitmap image files form multilingual plain texts. Ctext2pgm is an application program of VFlib, and it supports about 30 languages. We also introduce a language education system for various languages. This is an example of the multilingual system using internet imaging.
We are now developing a brain computer with algorithm acquisition function, where a two-level structure is introduced to connect pattern with (meta-)symbol, because we know how to realize algorithm acquisition on symbols. At Level 1 we use a conventional learning method on neural networks, but, at Level 2, we develop a new learning algorithm AST, where an automation-like algorithm with a neural network learning is introduced. This is powerful enough to realize an automatic algorithm acquisition. We will state a two-level structure and the AST learning algorithm. We focus on real-time image understanding which is a realization of human brain with eyes. We will summarize the features of our developing artificial brain system as follows: 1) System for meta-symbol as well as pattern, 2) Architecture artificial memory model to satisfy the features of 1)-3), We introduce a two-level architecture, where the meta-symbol is introduced at Level 2 while the pattern is used for Level 1 as usual.
A two-level processing scheme for real-time image understanding is proposed, where an example-based reasoning in neural AI systems is introduced. The system has tow levels; component level and structure level. At the component level, an elementary pattern recognition is performed as in the conventional pattern recognition, while the syntax pattern recognition is done at the structure level. Both levels are essentially time-consuming. The pattern recognition assisted by syntax recognition reduces the total complexity of processes, and the system can perform a real-time image understanding, when the VLSI chips are introduced. As a result, we show a reasonable real-time image understanding scheme by introducing a neural pattern recognition at the component level and a case-based AI technique at the structure level.
We have designed a neuro-chip for Kohonen learning vector quantization (LVQ) algorithm, and fabricated it by gate-arrays, which includes 12 neurons/chip. We proposed a simplified version for Kohonen LVQ algorithm, because the gate-array restricts the number of transistors. Moreover, the fixed-point calculation is inevitable in neuro-chip. In this paper we demonstrate a good performance of our chip, which is used for bit-pattern image processing. For real-time systems learning can be done in real-time as well as i/o response. The neuro- chip can execute learning procedure (actually, Kohonen LVQ algorithm) in real-time. The first-version chip (already realized) can execute 32 bit patterns, but the second version will be enlarged to 256 bit pattern processing. The neurons become as much as chips are connected to a bus. The demonstration board using the first-version chips includes four chips, i.e., 48 neurons, which corresponds to 48 patterns recognition.
Light propagation loss of the micron size optical waveguides is found to be improved from 1.8 to 0.6 dB/cm by capping waveguides by Al film. Al micromirrors for changing the light propagation direction in the vertical and horizontal planes were fabricated. Various shapes of Al corner mirrors to change the light direction in the horizontal plane were investigated. The straight simple mirror at an angle of 45 degree(s) against the incident light has the largest reflectivity of 50%. Branched waveguides were also fabricated by using Al corner mirrors and resulted in the almost equal distribution of the light for three branches. Light emitting diodes (LEDs), micromirrors, waveguides and photodetectors have been integrated on a single chip and the signal transfer from the LED to the photodetector has been verified.
We have proposed a new three-dimensional optically coupled common memory (3D-OCC memory) to solve the problem of bus bottle neck in the multi-processor system with the shared memories. Three-dimensional-OCC memory consists of several memory layers vertically stacked and a block of data is simultaneously transferred among these memories using vertically optical interconnection. Three-dimensional-OCC memory acts as the real shared memory. Three-dimensional-OCC memory test chip has been fabricated using 2 micrometers CMOS technology. LEDs are integrated on the silicon test chip by using a newly developed micro-bonding technology. We observed the uniform photon emission from these LEDs. In addition, the basic operation of 3D-OCC memory for optical writing/electrical reading was confirmed using this test chip.
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