Hidden object games are a great opportunity to try your skills for concentration and focus. They are free; they are fun and very educational, and also appropriate for players of all ages. There is no need to download them, fell free to visit our web page unlimited times! Let the discovery begin!
On this web page you could find a large list of hidden object games that can answer to your appetite for discovering and adventure. For reminding, the main task in these games is to find hidden objects or pictures on the screen. You are usually given a list of names, shapes or other object descriptions, so you should find out these items, if you want to go to the next level.
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Maybe it seams easy for you, you concern yourself as a person that has discerning eye but these games are not as easy as they look. Some details are hidden so good that you need hours an hours of detective work. Sometimes they are about finding differences in 2 almost identical pictures, but some hidden object games are about searching for very tiny clues that lead to solving a great mystery.
Time the seconds that it takes the participants to solve the game and limit the time so that they have to try again if they don't meet the challenge to increase competition. On the last page, the participants are informed about their time and score.
To raise the stakes for you and your picture-hunting prowess, the game presents a time limit that replenishes for a certain amount each time you find the correct item. You'll have to look for items more quickly to recover your lost time, as you scour the image for numbers, alphabets, sushi and so much more. You can also aim for combos to boost your time recovery rate to hit that high score and unlock new stages.
And in more traditional authored-content games, time limits are just another resource parameter to vary in making an interesting game segment. Certainly possible to tune poorly and make a bad experience, but not any less legitimate than varying the terrain, or the types of opposition, etc.
Hidden object games are puzzle-based video games that require players to find specific objects hidden within complex scenes or images. These games often feature immersive storylines and a wide range of themes, from mystery and adventure to fantasy and romance.
Hidden object games are engaging due to their combination of puzzles, immersive stories, and the satisfaction of finding hidden items. The challenge of locating objects within detailed scenes keeps players entertained and mentally engaged.
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Terahertz waves offer advantages for nondestructive detection of hidden objects/defects in materials, as they can penetrate most optically-opaque materials. However, existing terahertz inspection systems face throughput and accuracy restrictions due to their limited imaging speed and resolution. Furthermore, machine-vision-based systems using large-pixel-count imaging encounter bottlenecks due to their data storage, transmission and processing requirements. Here, we report a diffractive sensor that rapidly detects hidden defects/objects within a 3D sample using a single-pixel terahertz detector, eliminating sample scanning or image formation/processing. Leveraging deep-learning-optimized diffractive layers, this diffractive sensor can all-optically probe the 3D structural information of samples by outputting a spectrum, directly indicating the presence/absence of hidden structures or defects. We experimentally validated this framework using a single-pixel terahertz time-domain spectroscopy set-up and 3D-printed diffractive layers, successfully detecting unknown hidden defects inside silicon samples. This technique is valuable for applications including security screening, biomedical sensing and industrial quality control.
Apart from these limitations of existing terahertz imaging systems, the identification of hidden structural features in test volumes through the processing of large-pixel-count image data is, in general, bottlenecked and challenging to reach high throughputs needed in many applications (e.g., industrial quality control and security screening) due to the digital storage, data transmission, and image processing/classification requirements that are demanding for continuous imaging and sensing systems.
Here, we present a diffractive sensor (Fig. 1) that can rapidly detect hidden defects or objects within a target sample volume using a single-pixel spectroscopic terahertz detector. Unlike traditional approaches that involve point-by-point scanning and digital reconstruction of the target sample volume using a computer, this single-pixel diffractive sensor rapidly inspects the volume of the test sample illuminated with terahertz radiation, without the formation or digital processing of an image of the sample. Stated differently, rather than formulating the detection and classification of defects or hidden objects as part of a standard machine vision pipeline (i.e., image, digitize, and then analyze using a computer), instead, we treat the detection system as a coherent diffractive optical system that processes terahertz waves on demand, which can all-optically search for and classify undesired or unexpected sources of secondary waves generated by diffraction through hidden defects or structures. In this sense, the diffractive defect sensor can be considered an all-optical sensor for unexpected or hidden sources of secondary waves within a test volume, which are detected through a single-pixel spectroscopic detector. Our design is comprised of a series of diffractive layers, optimized to modify the spectrum of the terahertz radiation scattered from the test sample volume according to the absence or presence of hidden structures or defects. The diffractive layers are jointly optimized using deep learning, and contain tens of thousands of subwavelength phase features. Once their deep learning-based training is complete (which is a one-time effort), the resulting diffractive layers are physically fabricated using 3D printing or additive manufacturing, which forms an optical neural network31,32,33,34,35,36,37,38,39,40,41,42,43,44. When the test object volume is illuminated with terahertz radiation, the scattered terahertz waves from the object volume are all-optically processed by the diffractive network and sampled by a single-pixel spectroscopic detector at the output aperture of the system. The measured spectrum reveals the existence of hidden defects/structures within the sample volume all-optically, without the need for raster scanning or any image reconstruction or processing steps. Since these target structures or defects of interest are hidden within a solid volume, traditional machine vision approaches that operate at visible wavelengths cannot provide an alternative approach for these tasks. We demonstrated a proof-of-concept of this diffractive terahertz sensor by detecting hidden defects in silicon samples, which were prepared by stacking two wafers; one wafer containing etched defects and the other wafer covering the defective regions. The diffractive layers were designed to introduce a differential variation in the peak spectral intensity near two predetermined terahertz wavelengths. This diffractive defect sensor was realized using a single-pixel THz-TDS system with a plasmonic nanoantenna-based source45,46 generating pulsed terahertz illumination and a plasmonic nanoantenna-based detector47 sampling the terahertz spectrum at the output aperture. We numerically analyzed the performance of our diffractive defect sensor by evaluating its detection sensitivity as a function of the size and the position of the hidden defects within the detection field-of-view (FOV), also covering small feature sizes that are close to the diffraction limit of light. We fabricated the optimized diffractive layers using a 3D printer and conducted experimental tests for hidden defect detection. Our experimental results on silicon wafers with various unknown defect sizes and positions showed a good agreement with our numerical analysis, successfully revealing the presence of unknown hidden defects.
Although the diffractive defect sensors reported in this work were primarily designed for the terahertz band, the underlying concept and design approaches are also applicable for defect detection in other parts of the spectrum, including infrared, visible, and X-ray. These unique capabilities of performing computational sensing without a digital computer or the need for creating a digital 3D image will inspire the development of new task-specific all-optical detection systems and smart sensors. These systems can find diverse applications, such as industrial manufacturing and quality control, material inspection, detection/classification of hidden objects, security screening, and anti-counterfeiting measures. The non-destructive and non-invasive nature of this technology platform also makes it a valuable tool for sensitive applications, e.g., cultural heritage preservation and biomedical sensing. We believe that this framework can deliver transformative advances in various fields, where defect detection and materials diagnosis are of utmost importance.
Our reported approach demonstrates all-optical detection of hidden structures within 3D samples, enabled by a single-pixel spectroscopic terahertz detector, entirely eliminating the need to scan the samples or create, store, and digitally process their images. Our design employs an optical architecture featuring a passive diffractive encoder that generates structured illumination impinging onto the 3D sample of interest, coupled with a diffractive decoder that performs space-to-spectrum transformation, achieving defect detection based on the optical fields scattered from the sample volume. Leveraging this synergy between the two diffractive networks and their joint training/optimization, this single-pixel defect processor offers distinct advantages compared to the existing terahertz imaging and sensing systems used for the same purpose. First, the hidden defect detection is accomplished using a single-pixel spectroscopic detector, eliminating the need for a focal plane array or raster scanning, thus greatly simplifying and accelerating the defect detection process. Second, the diffractive layers we employ are passive optical components, enabling our diffractive defect sensor to analyze the test object volume without requiring any external power source except for the terahertz illumination and single-pixel detector. Third, our all-optical end-to-end detection process negates the need for memory, data/image transmission, or digital processing using e.g., a graphics processing unit (GPU), resulting in a high-throughput defect detection scheme. Overall, these characteristics render our single-pixel diffractive terahertz sensors particularly well-suited for high-throughput screening applications such as in industrial settings, e.g., manufacturing and security. These applications require high-throughput defect detection, where the hidden defects or objects of interest are often rare, but critically important to catch. Unlike conventional imaging-based methods, which are often hindered by the 3D image data overload due to redundant information and limited frame rates of 2D image sensors, our non-imaging and single-pixel defect detection approach can deliver markedly higher sensing throughput while offering cost-effectiveness and simplicity. 17dc91bb1f
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