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Image Recognition Processors Visconti™: Features

Feature 1: Parallel execution of up to eight image recognition applications

This is an example of simultaneous, parallel execution of multiple applications.

The Media Processing Engine (MPE) is suitable for image recognition and other multimedia processing. It is a very long instruction word (VLIW) machine that can issue multiple instructions simultaneously. It consists of Toshiba's proprietary low-power 32-bit RISC core called a Media Embedded Processor (MeP) and a coprocessor suitable for multimedia processing called Image Recognition VLIW Coprocessor 2 with FPU (IVC2F). IVC2F can execute one or two SIMD (single instruction stream, multiple data stream) instructions, which perform one or two operations on multiple data in parallel. The IVC2F can efficiently perform double-precision floating-point operations. The Visconti™ Family can execute multiple applications simultaneously* because of its multi-core configuration containing multiple MPEs.

* See the product specifications for details.

Visconti™4 Block Diagram

TMPV760 Block Diagram

Feature 2: Image processing solutions that combine high speed and low power consumption

Image processing solutions that combine high speed and low power consumption

The Visconti™ Family contain image processing accelerators that perform the image processing required frequently in each step of image recognition at very high speed with low power consumption. The Visconti™ Family contain an affine transformation accelerator, which corrects any distortion of a fish-eye or wide-angle lens and performs point-of-view transformation. In addition, the Visconti™ Family contain filter accelerators, which perform noise reduction, edge detection, color space conversion and other image processing. The Visconti™ Family also include a histogram accelerator that creates histograms, and a matching accelerator that performs disparity calculation for a stereo camera system and motion detection. Furthermore, the Visconti™ Family contain a HOG accelerator, which handles Histogram of Oriented Gradients (HOG) / Toshiba Co-occurrence Histograms of Oriented Gradients (CoHOG) features that are suitable for robust human recognition. These processors can detect both moving and stationary pedestrians in real time. The Visconti™4 Series incorporates an Enhanced CoHOG accelerator that combines luminance-based CoHOG feature descriptors with color-based feature descriptors obtained using a newly developed technique. It delivers a significant improvement in nighttime image recognition.

Feature 3: Toshiba’s original pattern recognition using multiple types of color features (Visconti™4)

Toshiba’s original pattern recognition using multiple types of color features (TMPV760 Series)

In addition to the Toshiba-original luminance-based CoHOG, the image recognition accelerator contained in the Visconti™4 processes four kinds of feature quantities: color-based CoHOG, Co-occurrence Histograms of Pairs of Edge Orientations and Color Differences (CoHED), Co-occurrence Histograms of Color Differences (CoHD), and Color Histograms. This accelerator provides high recognition performance even at nighttime and at scenes with less luminance differences between objects and the background. The Visconti™4 realizes nighttime pedestrian recognition as reliable as daytime pedestrian recognition available with conventional vision systems.

Feature 4: General obstacle detection using 3D reconstruction technology (Visconti™4)

Detection of Road Obstacles Using 3D Reconstruction Technique

While the Visconti™2 and Visconti™3 Series support the detection of pedestrians, vehicles and other objects using motion detection and pattern recognition, the SfM accelerator of the Visconti™4 Series also makes it possible to detect general stationary obstacles such as fallen objects, fallen rocks and landslides without pre-learned object models. The Structure from Motion (SfM) accelerator provides three-dimensional (3D) reconstruction, a process of estimating 3D structures (distance, height and width) from 2D image sequences using a monocular (i.e., single) camera. 

This is an explanatory diagram of the general obstacle detection using 3D reconstruction technology.
* MIPI is a registered trademark owned by the MIPI Alliance.
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