On the Design of a Photo Beauty Measurement Mechanism Based on Image Composition and Machine Learning

In this chapter, we propose a machine learning scheme on how to measure the beauty of a photo. Different from traditional measurements that focus on the quality of captured signals, the beauty of photos is based on high-level concepts from the knowledge of photo aesthetics. Because the concept of beauty is mostly defined by human being, the measurement must contain some knowledge obtained from them. Therefore, our measurement can be realized by a machine learning mechanism, which is trained by collected data from the human. There are several computational aesthetic manners used for building a photo beauty measurement system, including low-level feature extraction, image composition analysis, photo semantics parsing, and classification rule generation. Because the meaning of beauty may vary from different people, the personal preference is also taken into consideration. In this chapter, the performance of two computational aesthetic manners for the perception of beauty is evaluated, which are based on image composition analysis and low-level features to determine whether a photo meets the criterion of a professional photographing via different classifiers. The experimental results manifest that both decision tree and multilayer perceptron-based classifiers attain high accuracy of more than 90% for evaluation

Development of a Fingertip Glove Equipped with Magnetic Tracking Sensors

In this paper, we present the development of a data glove system based on fingertip tracking techniques. To track the fingertip position and orientation, a sensor module and two generator coils are attached on the fingertip and metacarpal of the corresponding finger. By tracking the fingertip, object manipulation tasks in a virtual environment or teleoperation system can be carried out more precisely, because fingertips are the foremost areas that reach the surface of an object in most of grasping processes. To calculate the bending angles of a finger, we also propose a method of constructing the shape of the finger. Since the coils are installed on the fingertips and metacarpals, there is no contact point between the sensors and finger joints. Hence, the shape of the sensors does not change as the fingers are bending, and both the quality of measurement and the lifetime of the sensors will not decrease in time. For the convenience of using this glove, a simple and efficient calibration process consisting of only one calibration gesture is also provided, so that all required parameters can be determined automatically. So far, the experimental results of the sensors performing linear movement and bending angle measurements are very satisfactory. It reveals that our data glove is available for a man-machine interface

An Autonomous Aesthetics-driven Photographing Instructor with Personality Prediction

[[abstract]]In this paper, an autonomous aesthetics-driven photographing instructor system is proposed, which gives instructions to help camera users to take good images. There are two kinds of instructions: image composition and personality feature enhancement. As for composition, a salient region is used to match the aesthetical template. To keep the personality of the autonomous photographing instructor system, the correlation between user types and image features is extracted through data mining on social networks, which is called “personality prediction.” The proposed system is run in realtime and workable on all mobile devices with cameras.[[notice]]補正完

On the Use of Augmented Reality Technology for Creating Interactive Computer Games

[[abstract]]In this paper, we design interactive games systems that adopt augmented reality (AR) technology. By virtue of a conventional webcam for capturing source images, we develop real-time visual tracking techniques based on edge detection and make 3D virtual objects display on our defined markers that are within the source images in the field of view (FOV) of the webcam. Two kinds of gaming interfaces are created for example: one is an AR based Monopoly game, and the other is an AR based fighting game. There are five classic human computer interface design methods considered to create the above AR based game systems. In the example of Monopoly games, we demonstrate how a traditional table game can be turned into an interactive computer game using the AR technology. We also list the advantages of a marker based approach and state why it is suitable for the interactive computer game. Further, the existing popular game consoles with different gaming interfaces are compared to the two AR based game systems. The comparison results reveal that our proposed AR based game systems are lower in cost and better in extensibility.[[notice]]補正完

A Virtual Handwriting Tablet Based on Pen Shadow Cues

[[abstract]]The handwriting tablet is an electronic product, which is a kind of human-computer interfaces acting as a computer input device comprising a set of a special pen and a tablet. The user holds the pen to draw contents within a region of the tablet as inputs, which imitates handwriting and is a replacement of mouse inputs. Some handwriting tablets not only imitate the handwriting and mouse functions, but also detect the pen tilts and pressures. The tilt and pressure information can be applied to some drawing software which can also render the thickness and depth of strokes. However, since the handwriting tablet is a piece of precise equipment, it has some drawbacks- fragile, not easy to carry, and the weight is often heavy. Therefore, in this paper, we propose a new concept based on the computer vision technology to simulate the handwriting tablet. We put a rectangular plane in the FOV of a video camera to emulate a tablet, and use a conventional pen to emulate the stylus. Many experiments have been made for evaluating the effectiveness of the proposed methods. The performance of such a virtual handwriting tablet is very satisfactory and encouraged.[[notice]]補正完

Vision-based Identification Extraction for Freight Containers

[[abstract]]The car license plates have regular formats and styles, and can be recognized by computer vision systems. However, some information that exclude from the alphanumerical characters of the car license plates are printed directly on a car body. Unlike car license plates, freight container's identifications have different colors and alignments. Some car bodies are not flat, such as trucks and freight containers, which increases the difficulty for character recognition. The character intervals in the identifications are varied from each other, and other information such as advertisement and logos are also marked on the car body, which make it hard to recognize characters too. Based on computer vision techniques, a fast and efficient method to extract the identification from freight containers is presented in this paper. The identification formats and styles conform to the standard of ISO/TC104. The information fliat does not pertain to part of the identification is eliminated during the processing. The most possible groups remaining in the image are extracted for identifications. Finally, an optical character recognition algorithm, Tesseract, is applied to recognize character images and then converted into texts. Thirty-five back surface images of freight containers are used to verify the effectiveness of the proposed method. The resolution of the images is set to 600 × 500. From the experimental results, we are encouraged that the accurate rate of identification extraction is over 90.37% and the average execution time is 12.91 seconds achieved by our proposed method.[[notice]]補正完

SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

[[abstract]]With the evolution of video surveillance systems, the requirement of video storage grows rapidly; in addition, safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events. As most of the scene in the surveillance video are redundant and contains no information needs attention, we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly. Our goal is to improve the condensation rate to reduce more storage size, and increase the accuracy in abnormal detection. As the trajectory feature is the key to both goals, in this paper, a new method for feature extraction of moving object trajectory is proposed, and we use the SOINN (Self-Organizing Incremental Neural Network) method to accomplish a high accuracy abnormal detection. In the results, our method is able to shirk the video size to 10% storage size of the original video, and achieves 95% accuracy of abnormal event detection, which shows our method is useful and applicable to the surveillance industry.[[notice]]補正完

Real-time Facial Expression Recognition in Image Sequences Using an AdaBoost-based Multi-classifier

In this paper, a highly automatic facial expression recognition system without choosing characteristic blocks in advance is presented. The system is able to detect and locate human faces in image sequences acquired in real environments. To achieve efficient facial expression recognition, we evaluate the performance of three different classifiers using multi-layer perceptrons (MLPs), support vector machines (SVMs), and Adaboost algorithms (ABAs). From the experimental outcomes, we can observe that the average recognition rates obtained from both ABAs and MLPs are better than that from SVMs, but the training of MLPs takes quite a long time. Comparatively, ABAs have an advantage of facilitating the speed of convergence, which are chosen as the core technique to implement our strong facial expression classifier. Through conducting many experiments, the statistics of performance reveals that the accuracy rate of our facial expression recognition system reaches more than 90% for a single kind or multiple kinds of expressions appearing in an image sequence.APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Oral session: 3D Synthesis and Expression (5 October 2009)

J.-L.: Efficient timeinterrupted and time-continuous collision detection among polyhedral objects in arbitrary motion

This paper presents efficient time-interrupted and time-continuous collision detection procedures for polyhedral objects which consist of convex polygons and perform arbitrary translating and/or rotating motions in a 3-D graphical environment. The time-interrupted collision detection procedure can find the exact position of a collision event in a time step while the time-continuous procedure can determine both the approximate time and position of a collision event. Each procedure first localizes the object-to-object collision events using a “space cell ” method, which divides the 3-D environment into small cells in order to reduce the number of object pairs required for collision detection. An “azimuthelevation map ” method is then proposed which rapidly selects the polygons within or crossing the overlap region between two possibly colliding objects by presorting their vertices in the spherical coordinates system. Subsequently, a divide-and-conquer method that takes advantage of a recursive scheme is devised to moderate the number of polygons which need to be checked by means of a polygon-to-polygon intersection test. To deal with time-interrupted and time-continuous collision detection, two respective polygon-to-polygon intersection testing methods based on a hierarchical scheme are developed to diminish the number of unnecessary computations. All the experiments were performed using computer simulation. So far, the experimental results from our proposed methods are very encouraging