Non-photorealistic, depth-based image editing

Recent works in image editing are opening up new possibilities to manipulate and enhance input images. Within this context, we leverage well-known characteristics of human perception along with a simple depth approximation algorithm to generate non-photorealistic renditions that would be difficult to achieve with existing methods. Once a perceptually plausible depth map is obtained from the input image, we show how simple algorithms yield powerful new depictions of such an image. Additionally, we show how artistic manipulation of depth maps can be used to create novel non-photorealistic versions, for which we provide the user with an intuitive interface. Our real-time implementation on graphics hardware allows the user to efficiently explore artistic possibilities for each image. We show results produced with six different styles proving the versatility of our approach, and validate our assumptions and simplifications by means of a user study.     

Segment inertial parameters of Korean adults estimated from three-dimensional body laser scan data

In this study, body segment parameters of Korean adults were estimated using the three-dimensional (3D) body scan data from the SizeKorea database. Mass-inertial parameters and segment dimensions of 40 male subjects and 40 female subjects (18-59 years old) were estimated using a 16-segment model under the assumption that each segment has a constant density distribution after reconstructing the scan data. Therefore, several sets of linear regression functions based on gender, stature, and weight were established, which provided a convenient method for estimating body segment parameters of Korean adults. The obtained mass ratios of body segments were compared with those for Russians reported by Zatsiorsky and Seluyanov (1983) and for those for Chinese and Germans reported by Shan and Bohn (2003), in which the same 16-segment model was used. In addition, the results were compared with the Korean data results reported by [Jung, 1993] and [Lim, 1994], and Park et al. (1999). These comparisons revealed that Asians have larger head mass ratios and smaller lower limb mass ratios than Caucasians, while the differences in the head mass ratios between males and females from the same ethnic groups were not significant. Females were found to have larger mass ratios for the lower torso and smaller mass ratios for the upper torso, middle torso, upper arm, forearm, foot, and hand, as compared to males from the same ethnic groups. In addition, the data obtained by different researchers were compared, thereby showing high reproducibility of our method.

Relevance to industry

The obtained segment parameters can be used to define digital human models and applied to the fields of ergonomics and biomechanics, such as for workspace design, furniture design, vehicle interior design, and human movement analysis.     

Coupling image processing and stress analysis for damage identification in a human premolar tooth

Non-carious cervical lesions are characterized by the loss of dental hard tissue at the cement-enamel junction (CEJ). Exceeding stresses are therefore generated in the cervical region of the tooth that cause disruption of the bonds between the hydroxyapatite crystals, leading to crack formation and eventual loss of enamel and the underlying dentine.Damage identification was performed by image analysis techniques and allowed to quantitatively assess changes in teeth. A computerized two-step procedure was generated and applied to the first left maxillary human premolar. In the first step, dental images were digitally processed by a segmentation method in order to identify the damage. The considered morphological properties were the enamel thickness and total area, the number of fragments in which the enamel is chipped. The information retrieved by the data processing of the section images allowed to orient the stress investigation toward selected portions of the tooth. In the second step, a three-dimensional finite element model based on CT images of both the tooth and the periodontal ligament was employed to compare the changes occurring in the stress distributions in normal occlusion and malocclusion. The stress states were analyzed exclusively in the critical zones designated in the first step.The risk of failure at the CEJ and of crack initiation at the dentin-enamel junction through the quantification of first and third principal stresses, von Mises stress, and normal and tangential stresses, were also estimated.     

Towards human motion capture from a camera mounted on a mobile robot

This article describes a multiple feature data fusion applied to a particle filter for marker-less human motion capture (HMC) by using a single camera devoted to an assistant mobile robot. Particle filters have proved to be well suited to this robotic context. Like numerous approaches, the principle relies on the projection of the model's silhouette of the tracked human limbs and appearance features located on the model surface, to validate the particles (associated configurations) which correspond to the best model-to-image fits. Our particle filter based HMC system is improved and extended in two ways. First, our estimation process is based on the so-called AUXILIARY scheme which has been surprisingly seldom exploited for tracking purpose. This scheme is shown to outperform conventional particle filters as it limits drastically the well-known burst in term of particles when considering high dimensional state-space. The second line of investigation concerns data fusion. Data fusion is considered both in the importance and measurement functions with some degree of adaptability depending on the current human posture and the environmental context encountered by the robot. Implementation and experiments on indoor sequences acquired by an assistant mobile robot highlight the relevance and versatility of our HMC system. Extensions are finally discussed.     

Good programming in transactional memory : Game theory meets multicore architecture

In a multicore transactional memory (TM) system, concurrent execution threads interact and interfere with each other through shared memory. The less interference a thread provokes the better for the system. However, as a programmer is primarily interested in optimizing her individual code’s performance rather than the system’s overall performance, she does not have a natural incentive to provoke as little interference as possible. Hence, a TM system must be designed compatible with good programming incentives (GPI), i.e., writing efficient code for the overall system should coincide with writing code that optimizes an individual thread’s performance. We show that with most contention managers (CM) proposed in the literature so far, TM systems are not GPI compatible. We provide a generic framework for CMs that base their decisions on priorities and explain how to modify Timestamp-like CMs so as to feature GPI compatibility. In general, however, priority-based conflict resolution policies are prone to be exploited by selfish programmers. In contrast, a simple non-priority-based manager that resolves conflicts at random is GPI compatible.     

3D registration of human face using evolutionary computation and Kriging interpolation

This paper proposes a fast and robust 3D human face geometric data registration strategy dedicated for image-guided medical applications. The registration scheme is composed of a coarse transformation stage and a fine-tuning stage. In the first stage, fuzzy c-mean is used to reduce the data amount of template 3D image, and evolutionary computation is implemented to find optimal initial pose for the Iterative Closest Point plus k-dimensional (KD) tree scheme. In the second stage, the huge reference image data are replaced by a Kriging model. The time-consuming search for corresponding points in evaluating the degree of misalignment is substituted by projecting the points in the template image onto the model. To illustrate the validity and applicability of the proposed approach, a problem composed of 174 635 points reference image and an 11 280 points template image is demonstrated. Computational results show that our approach accelerates the registration process from 1361.28 seconds to 432.85 seconds when compared with the conventional ICP plus K-D tree scheme, while the average misalignment reduces from 11.35 mm to 2.33 mm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Effective indices to characterize short sequences of human random generations

Brain impediments such as dementia are a serious problem today. It would be very useful if software for private diagnosis were available. In this paper, we show the effectiveness of the human random generation test (HRG) for such software, and propose a set of four indices to be used for classifying the HRG data. Human-generated random numbers have strong characteristics compared to computer-generated random numbers, and these are known to be correlated to the individual characters of the subjects. However, analysis using the correlation dimension or HMM requires a long data sequence, and thus is not suitable for diagnoses. We therefore focus on short sequences of HRG and search for effective indices to detect signs of brain disability hidden in the HRG data. We studied data from subjects of different age groups, and successfully differentiated the data from the different groups. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Color system for skin color extraction

This paper describes a skin color extraction method which has high speed and high accuracy. There were many systems which could achieve the requirements, but they were useless due to tough limitations such as using two cameras to recognize correct coordinates, running only a specific background, wearing several gears, learning the users detailed information in advance, making a personalized database, and so on. In contrast, our approach, which uses the YIQ color system with a background estimation method for skin color extraction, achieved a good performance more than 30 fps, with strong robustness in complicated and poorly illuminated environments. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Proteome alterations induced in human white blood cells by consumption of Brussels sprouts: Results of a pilot intervention study

Epidemiological studies indicate a correlation of cruciferous vegetables consumption with reduced incidence of cancer. This study was designed to investigate molecular mechanisms, which may help to understand the beneficial effects of Brussels sprout consumption. In order to avoid the limitations of in vitro model systems, we performed a dietary intervention study with five participants. We investigated, whether sprout consumption affects the proteome profile of primary white blood cells. In order to achieve maximal sensitivity in detecting specific adaptive proteome alterations, we metabolically labelled freshly isolated cells in the presence of ³⁵S‐methionine/cysteine and performed autoradiographic quantification of protein synthesis. Proteins were separated by 2‐DE and spots of interest were cut out, digested and identified by MS. After the intervention, we found a significant up‐regulation of the synthesis of manganese superoxide dismutase (1.56‐fold) and significant down‐regulation of the synthesis of heat shock 70 kDa protein (hsp70; 2.27‐fold). Both proteins play a role in malignant transformation of cells. Hsp‐70 is involved in the regulation of apoptosis, which leads to elimination of cancer cells, while SOD plays a key role in protection against reactive oxygen species mediated effects. Our findings indicate that the alteration of the synthesis of these proteins may be involved in the anticarcinogenic effects of cruciferous vegetables, which was observed in earlier laboratory studies with animals.     

Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression

In this paper, we propose the application of the Kernel Principal Component Analysis (PCA) technique for feature selection in a high-dimensional feature space, where input variables are mapped by a Gaussian kernel. The extracted features are employed in the regression problems of chaotic Mackey–Glass time-series prediction in a noisy environment and estimating human signal detection performance from brain event-related potentials elicited by task relevant signals. We compared results obtained using either Kernel PCA or linear PCA as data preprocessing steps. On the human signal detection task, we report the superiority of Kernel PCA feature extraction over linear PCA. Similar to linear PCA, we demonstrate de-noising of the original data by the appropriate selection of various nonlinear principal components. The theoretical relation and experimental comparison of Kernel Principal Components Regression, Kernel Ridge Regression and ε-insensitive Support Vector Regression is also provided.