Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Detecting humans in films and videos is a challenging problem owing to the motion of the subjects, the camera and the background and to variations in pose, appearance, clothing, illumination and background clutter. We develop a detector for standing and moving people in videos with possibly moving cameras and backgrounds, testing several different motion coding schemes and showing empirically that orientated histograms of differential optical flow give the best overall performance. These motion-based descriptors are combined with our Histogram of Oriented Gradient appearance descriptors. The resulting detector is tested on several databases including a challenging test set taken from feature films and containing wide ranges of pose, motion and background variations, including moving cameras and backgrounds. We validate our results on two challenging test sets containing more than 4400 human examples. The combined detector reduces the false alarm rate by a factor of 10 relative to the best appearance-based detector, for example giving false alarm rates of 1 per 20,000 windows tested at 8% miss rate on our Test Set 1.

/pdf/human-detection-using-oriented-histograms-of-flow-and-4y62o7njxp.pdf

Human detection using oriented histograms of flow and appearance

Home PURPOSE OF THE CENTER: To develop the center to address state-of-the-art research, create innovating educational programs, and support technology transfers using commercially viable results to assist the Army Research Laboratory to develop the next generation Future Combat System in the telecommunications sector that assures prevention of perceived threats, and Non Line of Sight/Beyond Line of Sight lethal support.

How to… home page

This protocol describes the steps needed to perform quantitative statistical colocalization on two-color confocal images, specifically of plant cells. The procedure includes a calibration test to check the chromatic alignment of the confocal microscope. A software tool is provided to calculate the Pearson and Spearman correlation coefficients ('Pearson–Spearman correlation colocalization' ImageJ plug-in) across regions of interest within the image. Steps are included to help the user practice using the software. The result is a quantitative estimate of the amount of colocalization in the images. Manual masking takes about 1–15 min per image, depending on the detail required, and calculating the correlation coefficients is almost instantaneous. Examples of suitable dyes for such two-color colocalization include Oregon Green or Alexa Fluor 488 dyes in the green range (excited with 488-nm laser line) and Alexa Fluor 555 dye in the red range (excited with 543-nm laser line).

Colocalization of fluorescent markers in confocal microscope images of plant cells

Evaluating the performance of optical flow algorithms has been difficult because of the lack of ground-truth data sets for complex scenes We describe a simple modification to a ray tracer that allows us to generate ground-truth motion fields for scenes of arbitrary complexity The resulting flow maps are used to assist in the comparison of eight optical flow algorithms using three complex, synthetic scenes Our study found that a modified version of Lucas and Kanade’s algorithm has superior performance but produces sparse flow maps Proesmans et al’s algorithm performs slightly worse, on average, but produces a very dense depth map

/pdf/recovering-motion-fields-an-evaluation-of-eight-optical-flow-3xlfqyexuc.pdf

Recovering Motion Fields: An Evaluation of Eight Optical Flow Algorithms

The AUX1 and PIN auxin influx and efflux facilitators are key regulators of root growth and development. For root gravitropism to occur, AUX1 and PIN2 must transport auxin via the lateral root cap to elongating epidermal cells. Genetic studies suggest that AXR4 functions in the same pathway as AUX1. Here we show that AXR4 is a previously unidentified accessory protein of the endoplasmic reticulum (ER) that regulates localization of AUX1 but not of PIN proteins. Loss of AXR4 resulted in abnormal accumulation of AUX1 in the ER of epidermal cells, indicating that the axr4 agravitropic phenotype is caused by defective AUX1 trafficking in the root epidermis.

https://science.sciencemag.org/content/sci/312/5777/1218.full.pdf

AXR4 Is Required for Localization of the Auxin Influx Facilitator AUX1

This paper describes a robot system for the automatic pruning of grape vines. A mobile platform straddles the row of vines, and it images them with trinocular stereo cameras as it moves. A computer vision system builds a three-dimensional (3D) model of the vines, an artificial intelligence (AI) system decides which canes to prune, and a six degree-of-freedom robot arm makes the required cuts. The system is demonstrated cutting vines in the vineyard. The main contributions of this paper are the computer vision system that builds 3D vine models, and the test of the complete-integrated system. The vine models capture the structure of the plants so that the AI system can decide where to prune, and they are accurate enough that the robot arm can reach the required cuts. Vine models are reconstructed by matching features between images, triangulating feature matches to give a 3D model, then optimizing the model and the robot's trajectory jointly (incremental bundle adjustment). Trajectories are estimated online at 0.25 m/s, and they have errors below 1% when modeling a 96 m row of 59 vines. Pruning each vine requires the robot arm to cut an average of 8.4 canes. A collision-free trajectory for the arm is planned in intervals of 1.5 s/vine with a rapidly exploring random tree motion planner. The total time to prune one vine is 2 min in field trials, which is similar to human pruners, and it could be greatly reduced with a faster arm. Trials also show that the long chain of interdependent components limits reliability. A commercially feasible pruning robot should stop and prune each vine in turn.

A Robot System for Pruning Grape Vines

This letter introduces the LOOP binary descriptor (local optimal-oriented pattern) that encodes rotation invariance into the main formulation itself. This makes any post processing stage for rotation invariance redundant and improves on both accuracy and time complexity. We consider fine-grained lepidoptera (moth/butterfly) species recognition as the representative problem since it involves repetition of localized patterns and textures that may be exploited for discrimination. We evaluate the performance of LOOP against its predecessors as well as few other popular descriptors. Besides experiments on standard benchmarks, we also introduce a new small image dataset on NZ Lepidoptera. LOOP performs as well or better on all datasets evaluated compared to previous binary descriptors. The new dataset and demo code of the proposed method are available through the lead author's academic webpage and GitHub.

Steven Mills

Papers

Colocalization of fluorescent markers in confocal microscope images of plant cells

Recovering Motion Fields: An Evaluation of Eight Optical Flow Algorithms

AXR4 Is Required for Localization of the Auxin Influx Facilitator AUX1

A Robot System for Pruning Grape Vines

LOOP Descriptor: Local Optimal-Oriented Pattern