Generic Visual Perception Processor Gvpp Pdf FilesUS Patent # 7,2. 12,6. Automatic perception method and device. Malloy & Malloy, P. A. Description. The invention relates to a. The invention will be described more in detail with reference to the appended. Generic Visual Perception Processor Gvpp Pdf ReaderFIG. 1 is a representation of the histogram calculation unit according to the invention, in its context. FIG. 2 is a representation of the input video signal, processed by the device and the method of the invention and of the control signals generated by a sequencer. FIG. 3 is a diagram representing a passive histogram calculation unit. FIG. 4 is a diagram representing a self- adapting histogram calculation unit according to the invention with the anticipation and learning functionalities.
Presentation PDF,DOC and PPT for 2013 Engineers World ' Where engineers speak ' Latest Seminar : * latest computer science & engineering seminar topics 2013. FIG. 5 is a diagram representing signals processed by the calculation unit of FIG. FIG. 4 in master mode. FIG. 7 is the flow chart of the software controlling the calculation unit of FIG. ![]() Generic Visual Perception Processor GVPP Word Sense Disambiguation X- Internet Wardriving Get More Information about Generic Visual Perception Processor (GVPP) PDF by visiting this link. The 'generic visual perception processor (GVPP)' has been developed after 10. We provide Latest Information Technology IT Seminar Topics,Presentation Topics and Advanced Research Topics PPT,seminar topic for IT pdf. Engineering interview questions,multiple choice questions,objective type questions,seminor topics,lab viva questions. The Psikharpax Project: Towards Building an Artificial Rat Jean-Arcady Meyer *, Agn. Sensor fusion will be realized through the use of GVPP (Generic Visual Perception Processor), a biomimetic chip dedicated to low-level real. ![]() FIG. 8 is the flow chart of the insertion software of the curve zone. FIG. 9 is the flow chart of the initialisation software (generation of the command `INIT`). FIG. 1. 0 is the flow chart of the statistical calculation software (use of the command `WRITE`). FIG. 1. 1 is a flow chart of the end of the processing (use of the command `END`). FIG. 1. 2 is a representation of the essential elements of the histogram calculation unit with a self- adapting functionality. FIGS. 1. 3 and 1. FIGS. 1. 3a and 1. FIG. 1. 4 is a representation of the elements of a histogram calculation unit producing values POSMOY. FIG. 1. 5 is a diagram representing the essential elements of the self- adapting histogram calculation unit with anticipation according to a first method. FIG. 1. 5a is a diagram similar to FIG. FIG. 1. 6 is a diagram of the classifier memory. FIG. 1. 7 is a diagram representing the essential elements of the self- adapting histogram calculation unit with anticipation according to a second method. FIG. 1. 8 is a detailed representation of the classifier memory with a bit- operated elementary calculation automaton. FIG. 1. 9 is a representation of an elementary anticipation calculation automaton. FIG. 2. 0 is a schematic representation of the anticipation process. FIG. 2. 1 is the flow chart of the anticipation implementation software. FIG. 2. 2 is a representation of the time coincidences unit. FIG. 2. 3 is a flow chart representation of a field programmable gate array (FPGA) used as a time coincidences unit. FIG. 2. 4 is the register- based representation, limited to one row of the system, of FIG. FIGS. 2. 6 and 2. FIG. 2. 8 is a schematic representation of the statistical visualisation device. FIG. 2. 9 is an example of the result obtained using the visualisation produced by the device of FIG. FIG. 3. 1 is the representation of the use of a single programmable histogram calculation unit with a multiplexer enabling the calculation unit to process a plurality of parameters. FIG. 3. 1a is the representation of a histogram calculation unit; also called electronic spatio- temporal neuron. FIG. 3. 2 represents a set of histogram calculation units with programmable input control in their context of usage thereby constituting a functional entity. FIG. 3. 3 is a synthetic representation of a functional unit with the associated signal generator. FIG. 3. 4 corresponds to FIG. FIG. 3. 5 corresponds to FIG. FIG. 3. 6 is a schematic representation of a signal generator fitted with controlled optics. FIG. 3. 7 shows the case of a three- source acquisition. FIG. 3. 8 is a representation of the application management interface (API). FIG. 3. 9 illustrates a sound signal processing device according to the invention. FIG. 4. 0 is a simplified representation of a device according to the invention. A, 1. B, . The. following description relates mainly to visual perception, although it can be adapted to other parameters. SL, ST, CLOCK) may come from a signal generator assembly 2 comprising a camera 2. CMOS imaging device 3. DATA(E) in this. application. T. sub. 1 and T. sub. I. sub. 1. 1. I. sub. DATA(A) whereof the structure is represented in FIG. The Analysis Memory. This histogram calculation unit 1 comprises an analysis memory 1. The Time Coincidences Unit. The histogram calculation unit also comprises a time coincidences unit 1. Extrapolation to any number of units is evident. Signal WRITE. For each signal WRITE, each histogram processing unit supplies to the bus, for each pixel, the output signal 1. A, . Signal INIT. During the signal INIT, the signal COUNTER that scans the values from 0 to n, resets the registers of the memory 1. FIG. First Embodiment of Classifier. With reference to FIG. WR receives the signal END and the address input ADDRESS receives the output signal of the address multiplexer 1. The memory 1. 18 of the classifier is thus updated. Second Embodiment of Classifier. FIG. 1. 3 represents an alternative embodiment of the classifier wherein a multiplexer 1. P to a statistical value Q, which can be prepared in various ways in. P can be compared to the respective values RMAX/2, RMAX, at a threshold B input from the outside and in proportion to the number of points NBPTS attached to this threshold by the divider 1. Third Embodiment of Classifier. FIGS. 1. 3a, 1. 3b, 1. The output multiplexer 1. ADDRESS, on. the output 1. At output, the information originating from these. Anticipation. In a preferred embodiment, in addition to real time updating, the histogram processing unit 1 performs an anticipation function. Calculation of the Global Variation of the Histogram. The test unit 1. 03 and the analysis output registers 1. POSMOY whereof the values POSMOY. POSMOY. sub. 1 for two successive frames are memorised. POSMOY. sub. 0 will now be described. Application of the Histogram Variation to the Anticipation (First Method). FIG. 1. 5 illustrates this first method. POSMOY. sub. 0 minus POSMOY. It will, of course, be apparent that any other function of the POSMOY values can be used as desired, such as y=ax. POSMOY, namely k. P. sub. 0- P. sub. P. sub. 0- P. sub. Application of the Histogram Variation to the Anticipation (Second Method). This second method is represented on FIG. Complex Time Coincidences Criteria. In a preferred embodiment, the time coincidences block is a memory that may contain several values forming together the time coincidences criterion R, any of which is capable of enabling the information carried by a pixel. Field Programmable Gate Area (FPGA) 4. The Learning Mode. The time coincidences block can be programmed externally by an instruction given by an application management interface. Space Transform` unit is referred to as 3. A, B, C, D, E . The association of spatial (generally two in number) as well as temporal. A, B, C, D, E . D and E can represent the co- ordinates P. P. sub. 2 of the pixel considered. A, each histogram calculation unit 1. A, 1. sub. B, . 3. DATA(A) . DATA(E) feed an input multiplexer 5. E that are addressed by a bus 5. SELECT. 3. 2 represents a complete device comprising, for exemplification purposes, a set of sixteen such polyvalent histogram calculation units. FIG. 3. 1) according to an embodiment of this invention. DATA(A) and DATA(C)) during each frame. FIG. 3. 2, is implemented to process parameters associated with a perception domain other than the. The signal generating device preferably provides. FIG. For example, in one embodiment, the device 6. CMOS imaging device 5. CMOS imaging devices. V1, V2 and V3 that can represent a three- dimensional. DATA(E) to real parameters of the scene observed. Parameters: Number of the block affected, signal to be selected. GET Block. 3 NPTS Input- R0: Number of the block R1: Input parameter Output- R0: Value resulting from this parameter LEARN: Role: A block switches to the learning mode. VALIDATION = ENABLING FIG. DEPART MAITRE = START MASTER Registres = registers FIG. Sequence courbe: curve sequence FIG. INITIALISATION SEQUENCE: SEQUENCE. INITIALISATION FIG. CALCUL STATISTIQUE: STATISTICAL CALCULATION Classifier 1. FIG. 1. 1 FIN SEQUENCE: END OF SEQUENCE Mise a jour du classifieur: Updating the classifier Nouveau calcul de POSMOY: new calculation of POSMOY FIG. CHOIX: CHOICE SEUIL. THRESHOLD FIG. 1. Borne: terminal FIG. Attente: standby FIG. Apprentissage: learning Classification automatique: automatic classification.
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