Satoshi Naoi

A compact navigation system using image processing and fuzzy control

A visual control system for an unmanned vehicle is developed. The system uses dynamic image processing and fuzzy logic control. It quickly recognizes markers along a road and steers the vehicle. The markers are detected in real time by pipeline processing in the color identification processor and logical filter. The marker sequence is recognized by an improved Hough transform, then fuzzy theory decides the steering angle. To use the information on the movement of the vehicle, the authors constructed fuzzy inference rules on how position changes with time. The authors developed an LSI chip for the logical filter to make the system compact and practical (A4 size*10 cm). This system is mounted on a vehicle, and it steered the vehicle around a test track successfully.<<ETX>>

Automatic filter selection using image quality assessment

We present a method for automatically selecting the best filter to treat poor quality printed documents using image quality assessment. We introduce five quality measures to obtain information about the quality of the images, and morphological filters to improve their quality. A training set of 370 images was used to develop the system. Experimental results on the test set show a significant improvement in the recognition rate from 73.24% using no filter at all to 93.09% after applying a filter that was automatically selected.

Robust Vanishing Point Detection for MobileCam-Based Documents

Document images captured by a mobile phone camera often have perspective distortions. In this paper, fast and robust vanishing point detection methods for such perspective documents are presented. Most of previous methods are either slow or unstable. Based on robust detection of text baselines and character tilt orientations, our proposed technology is fast and robust with the following features: (1) quick detection of vanishing point candidates by clustering and voting on the Gaussian sphere space, and (2) precise and efficient detection of the final vanishing points using a hybrid approach, which combines the results from clustering and projection analysis. The rectified image acceptance rate for Mobile Cam-based documents, signboards and posters is more than 98% with an average speed of about 100ms.

Effective text extraction and recognition for WWW images

Images play a very important role in web content delivery. Many WWW images contain text information that can be used for web indexing and searching. A new text extraction and recognition algorithm is proposed in this paper. The character strokes in the image are first extracted by color clustering and connected component analysis. A novel stroke verification algorithm is used to effectively remove non-character strokes. The verified strokes are then used to build the binary text line image, which is segmented and recognized by dynamic programming. Since text in WWW image usually has close relationship with webpage content, approximate string matching is used to revise the recognition result by matching the content in the webpage with the content in the image. This effective post-processing not only improves the recognition performance, but also can be used in other applications such like image - webpage paragraph corresponding.

Camera based degraded text recognition using grayscale feature

As the rapid progress of digital imaging technology, camera based character recognition receives more and more attentions. One challenge in camera based OCR is the recognition for degraded text. Conventional OCR engines usually recognize on binary image. However, the performance drops dramatically as the degradation level increases. In this paper, a new recognition method is proposed to recognize degraded character based on dual eigenspace decomposition and synthetic degraded data. Then, the degraded character string is segmented by the combination of binary and grayscale analysis. Experiments on single character and text string recognition prove the effectiveness of our method.

Improving Scene Text Detection by Scale-Adaptive Segmentation and Weighted CRF Verification

This paper presents a hybrid method for detecting and localizing texts in natural scene images by stroke segmentation, verification and grouping. To improve system performance, novelties on two aspects are proposed: 1) a scale-adaptive segmentation method is designed for extracting stroke candidates, and 2) a CRF model with pair-wise weight by local line fitting is designed for stroke verification. Moreover, color-based text region estimation is used to guide segmentation and verification more accurately. Experimental results on ICDAR 2005 competition dataset show that the proposed approach can detect and localize scene texts with high accuracy, even under noisy and complex backgrounds.

Beyond human recognition: A CNN-based framework for handwritten character recognition

Because of the various appearance (different writers, writing styles, noise, etc.), the handwritten character recognition is one of the most challenging task in pattern recognition. Through decades of research, the traditional method has reached its limit while the emergence of deep learning provides a new way to break this limit. In this paper, a CNN-based handwritten character recognition framework is proposed. In this framework, proper sample generation, training scheme and CNN network structure are employed according to the properties of handwritten characters. In the experiments, the proposed framework performed even better than human on handwritten digit (MNIST) and Chinese character (CASIA) recognition. The advantage of this framework is proved by these experimental results.

A Multi-Stage Strategy to Perspective Rectification for Mobile Phone Camera-Based Document Images

Document images captured by a mobile phone camera often have perspective distortions. Efficiency and accuracy are two important issues in designing a rectification system for such perspective documents. In this paper, we propose a new perspective rectification system based on vanishing point detection. This system achieves both the desired efficiency and accuracy using a multi-stage strategy: at the first stage, document boundaries and straight lines are used to compute vanishing points; at the second stage, text baselines and block aligns are utilized; and at the last stage, character tilt orientations are voted for the vertical vanishing point. A profit function is introduced to evaluate the reliability of detected vanishing points at each stage. If vanishing points at one stage are reliable, then rectification is ended at that stage. Otherwise, our method continues to seek more reliable vanishing points in the next stage. We have tested this method with more than 400 images including paper documents, signboards and posters. The image acceptance rate is more than 98.5% with an average speed of only about 60 ms.

Local Consistency Constrained Adaptive Neighbor Embedding for Text Image Super-Resolution

This paper proposes a robust single-image super-resolution method for enlarging low quality camera captured text image. The contribution of this work is twofold. First, we point out the non-local reconstruction problem in neighbor embedding based super-resolution by statistical analysis on an empirical data set. Second, we introduce a local consistency constraint to explicitly regularize the linear reconstruction process, and adaptively generate the most possible candidates for the high-resolution image patch. For the non-consistent candidates, we rely on its adjacent overlapping patches for capability verification. Experimental results demonstrate that our solution produces visually pleasing enlargements for various text images.

A Dual Pass Video Stabilization System Using Iterative Motion Estimation and Adaptive Motion Smoothing

In this paper, we propose a novel dual pass video stabilization system using iterative motion estimation and adaptive motion smoothing. In the first pass, the transformation matrix to stabilize each frame is returned. The global motion estimation is carried out by a novel iterative method. The intentional motion is estimated using adaptive window smoothing. Before the beginning of the second pass, we obtain the optimal trim size for a specific video based on the statistics of the transformation parameters. In the second pass, the stabilized video is composed according to the optimal trim size. Experimental results show the superior performance of the proposed method in comparison to other existing methods.