CN109559758A - A method of texture image is converted by haptic signal based on deep learning - Google Patents
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Abstract
The method that texture image is converted into haptic signal based on deep learning that the present invention relates to a kind of, belongs to artificial intelligence, signal processing technology field.Learning training texture image data first obtains the characteristic information of image, to classify to all kinds of texture recognitions；The 3-axis acceleration signal of material surface friction vibration is converted into spectral image using Fourier algorithm in short-term, then training obtains spectral generator；Classification information is combined with spectral generator, automatically generates the frequency spectrum of texture image, frequency spectrum is converted into the haptic signal of different classes of image, realizes the conversion of different texture image to haptic signal.Result is passed into palm by being linked into the touch feedback device inside mouse, region locating for mouse pointer is tested texture area, so that sliding mouse can feed back the material properties for understanding testee in real time.The true sense of touch similarity of transformation result and image texture of the invention is higher, and application scenarios are abundant, has high practical value.
Description
Technical field
The method that texture image is converted into haptic signal based on deep learning that the present invention relates to a kind of, belongs to artificial intelligence
Energy, signal processing technology field.
Background technique
With the development of global industry and the emergence of artificial intelligence, object Material Identification is in e-commerce, mechanical system
It makes and is widely used with many industrial circles such as intelligent robot.Current Material Identification is typically based on the texture of body surface
Image identifies the affiliated material (such as wooden, glass, plastics, steel and fiber) of objects in images.But it is based on texture maps
The Material Identification of picture is easy to be influenced by shooting environmental, and apparent difference apparent between difference and small class is usual in big class
Will lead to textural characteristics can distinction weaken, robustness reduce.In addition to this, texture image can not accurate response and material phase
The thingness of pass.Such as, it is based on texture image, the textures and material object of identical texture cannot be distinguished.
For different texture images, the vibrational feedback signal under corresponding tool interaction can allow people preferably to sense it
Tactile characteristics.There are decades so far about touch feeling model building research, commonly entering in the modeling of tactile is tool
State (such as speed of tool) and grain surface state (such as attribute of texture), output be then vibrating tactile signal.
The limitation of complex mappings and experimental tool level between being output and input due to haptic signal, by the state and line of tool
The state for managing imaging surface is difficult as input simultaneously, can feed back its correspondence well there is no a kind of experimental model at present
Haptic signal.In addition to this, the quality for generating result for tactile is evaluated, and is remained in artificial subjective feeling at present and is differentiated
On, there is no the appraisement systems of a set of objective quantification, this allows for none good judgment criteria of result of study.
Summary of the invention
The method that texture image is converted into haptic signal based on deep learning that the purpose of the present invention is to propose to a kind of, with
The shortcoming for overcoming prior art, the haptic signal for being converted to texture image are more nearly true sense of touch.
Method that texture image is converted into haptic signal proposed by the present invention based on deep learning, including following step
It is rapid:
(1) Adam's optimization is carried out to the depth residual error network for texture image training, the depth optimized using the Adam
Residual error network is trained texture image, obtains the label information C with texture image feature；
(2) tactile frequency spectrum diagram generator is obtained using the 3-axis acceleration signal of texture image, process is as follows:
(2-1) carries out Short Time Fourier Transform to 3-axis acceleration signal relevant to texture image, obtains texture image
Initial haptic spectrogram, which is carried out taking logarithm and normalized, obtains the tactile frequency spectrum of texture image
Figure；
(2-2) obtains a tactile frequency spectrum with the tactile spectrogram of above-mentioned texture image, one confrontation learning network of training
Diagram generator G；
(3) to the label information C of step (2) tactile frequency spectrum diagram generator G input one random signal Z and step (1), touching
Feel that frequency spectrum diagram generator G exports tactile spectrogram corresponding with texture image；
(4) it by Griffin (Griffin-Lim) method, is generated and texture image phase by the tactile spectrogram of step (3)
The audio signal of generation is carried out power amplification by corresponding audio signal, and amplified audio signal is obtained through touch feedback device
The haptic signal of texture image.
Method that texture image is converted into haptic signal proposed by the present invention based on deep learning, its advantage is that:
The method of the present invention obtains the characteristic information of image using resnet model learning training texture image data, thus
Classify to all kinds of texture recognitions；Using Fourier algorithm in short-term, the 3-axis acceleration signal of each material surface friction vibration is turned
Change spectral image into；Followed by DC-GAN training, these data obtain spectral generator；It is generated using classification information and frequency spectrum
Device, realization automatically generate the frequency spectrum of different texture image, are then turned the spectrum results of generation using Griffin-Lim algorithm
It changes the haptic signal of different classes of image into, realizes the conversion of different texture image to haptic signal.Final result is by connecing
The touch feedback device entered to inside mouse passes to palm, and region locating for mouse pointer is tested texture area, so that
Sliding mouse can feed back the material properties for understanding testee in real time.The transformation result of the method for the present invention and image texture
True sense of touch similarity is higher, and application scenarios are abundant, has high practical value.
Detailed description of the invention
Fig. 1 is the flow diagram of the method for the present invention.
Fig. 2 is GAN model involved in the method for the present invention.
Specific embodiment
Method that texture image is converted into haptic signal proposed by the present invention based on deep learning, flow diagram is such as
Shown in Fig. 1, comprising the following steps:
(1) Adam's optimization is carried out to the depth residual error network for texture image training, the depth optimized using the Adam
Residual error network is trained texture image, obtains the label information C with texture image feature；
(2) tactile frequency spectrum diagram generator is obtained using the 3-axis acceleration signal of texture image, process is as follows:
(2-1) carries out Short Time Fourier Transform to 3-axis acceleration signal relevant to texture image, obtains texture image
Initial haptic spectrogram, which is carried out taking logarithm and normalized, obtains the tactile frequency spectrum of texture image
Figure；
The tactile spectrogram of (2-2) with above-mentioned texture image, one confrontation learning network of training, training process model such as figure
Shown in 2, a tactile frequency spectrum diagram generator G is obtained；
(3) to the label information C of step (2) tactile frequency spectrum diagram generator G input one random signal Z and step (1), touching
Feel that frequency spectrum diagram generator G exports tactile spectrogram corresponding with texture image；
(4) it by Griffin (Griffin-Lim) method, is generated and texture image phase by the tactile spectrogram of step (3)
The audio signal of generation is carried out power amplification by corresponding audio signal, and amplified audio signal is obtained through touch feedback device
The haptic signal of texture image.
Below in conjunction with attached drawing, the contents of the present invention are discussed in detail:
As shown in Figure 1, the present invention the following steps are included:
The extraction of characteristics of image is classified:
Pretreatment: due to the difference of all kinds of initial data itself, it is possible that the problems such as sample is less, distracter is more.
The operation for needing to carry out texture image data cleaning and data enhancing (mirror image, rotation, scaling, random cropping), then passes through
Screening, which is rejected, to be obtained being suitable for trained texture image data.
Resnet network is constructed using skip connection residual error module, is made it possible to training and is obtained deeper time
Network, to obtain preferable as a result, the present invention uses depth residual error network Resnet as training network, tool in this step
The model depth of body can adjust according to different results.Meanwhile the network optimized approach used in this step network is most
Adam Adam optimization.Faster, learning effect is more effective for Adam convergence speed of the algorithm, and can correct other optimisation techniques
The problem of, such as learning rate disappear, restrained slow or high variance parameter update cause loss function fluctuation it is larger
Problem.Some other specific parameter of prototype network can be adjusted according to different situations.After the completion of final mask training i.e.
The label information C of texture image can be obtained.
Spectrogram is converted by feature:
Corresponding tactile frequency is obtained by Short Time Fourier Transform (STFT) first with the acceleration signal of raw data base
Spectrogram, STFT (short-time Fourier transform, Short Time Fourier Transform) is relevant with Fourier transformation one
Kind mathematic(al) manipulation, to determine the frequency and phase of its regional area sine wave of time varying signal.Through STFT treated signal tool
There is the localization property of time domain and frequency domain, the consequential signal after Fourier transform in short-term has been carried out taking logarithm and returned in this step
One change processing, then generates to obtain desired spectrogram according to these information, parameter used in other Short Time Fourier Transforms
It can be adjusted according to different actual conditions.
Operation above only obtains training data, and the purpose of this step be autonomously generated with reference to training data it is desired
Spectrogram.Confrontation model, i.e. GAN model are inspired from two game sides in the zero-sum two-person game in game theory, GAN model point
It is not production model (generative model) and discriminative model (discriminative model).Generate model G
The distribution of sample data is captured, discrimination model D is two classifiers, estimates that a sample is (rather than raw from training data
At data) probability.It can make G by the game training of G and D, ultimately generate image information.
It is DC-GAN that the present invention, which generates network using confrontation, and DCGAN is relatively good improvement after GAN, greatly
Improve GAN training stability and generate outcome quality.For training data, all individually training is primary for each major class,
Such benefit is that the diversity that ensure that in class in turn ensures otherness between class.
DC-GAN model is as shown in Figure 2.DC-GAN network is mainly made of discriminator D and generator G two parts.Generator
Input be that C and Z are formed, C is the image vector that label information is had obtained in preceding step, and Z is random noise (according to difference
The case where dimensional parameter is set).Discriminator D first passes through the segments sonogram data collection that initial data obtains and is trained, and foundation is commented
Sentence index system, the spectrogram for then obtaining generator is as input, to complete to the differentiation for generating sonograph and return
Class.
Convert tactile:
It converts the C that texture image label information is had obtained in the extraction classifying step of characteristics of image to as feature
The input of the generation model for the DC-GAN that training finishes in spectrogram step, the two, which is combined, can be autonomously generated inhomogeneity
The corresponding spectrogram of other texture image.
The sonograph generated in previous step is generated into corresponding audio signal using Griffin (Griffin-Lim) algorithm,
Griffin-Lim algorithm can produce the audio signal of generation well according to frequency spectrum come reconstruction signal after power amplifier
Raw touch feedback, final result pass to palm, area locating for mouse pointer by being linked into the touch feedback device inside mouse
Domain is tested texture area, so that sliding mouse can feed back the material properties for understanding testee in real time.
It is the process evaluated the haptic signal generated using the method for the present invention below, calculating process is as follows:
The frequency of the original haptic signal by Texture image synthesis is sampled first, and draws waveform, extracts waveform
The coordinate of figure center line is (x1i, y1i)；
Then waveform diagram is gone out by sample rendering to the haptic signal exported after generating model, extracts waveform diagram center
The coordinate of line is (x2i, y2i)。
Set similarity are as follows:
The value range of S is [0,1], and the tactile of the method for the present invention generation can be judged by the similarity of sampled signal
Signal quality, the more big then similarity of S value are higher.
Claims (1)
1. a kind of method that texture image is converted into haptic signal based on deep learning, it is characterised in that this method include with
Lower step:
(1) Adam's optimization is carried out to the depth residual error network for texture image training, the depth residual error optimized using the Adam
Network is trained texture image, obtains the label information C with texture image feature；
(2) tactile frequency spectrum diagram generator is obtained using the 3-axis acceleration signal of texture image, process is as follows:
(2-1) carries out Short Time Fourier Transform to 3-axis acceleration signal relevant to texture image, obtains the first of texture image
Beginning tactile spectrogram carries out taking logarithm and normalized to the tactile spectrogram, obtains the tactile spectrogram of texture image；
The tactile spectrogram of (2-2) with above-mentioned texture image, one confrontation learning network of training obtain a tactile spectrogram life
Grow up to be a useful person G；
(3) to the label information C of step (2) tactile frequency spectrum diagram generator G input one random signal Z and step (1), tactile frequency
Spectrogram generator G exports tactile spectrogram corresponding with texture image；
(4) by Griffin method, audio signal corresponding with texture image is generated by the tactile spectrogram of step (3), it will
The audio signal of generation carries out power amplification, and amplified audio signal obtains the tactile letter of texture image through touch feedback device
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| CN111796709A (en) * | 2020-06-02 | 2020-10-20 | 南京信息工程大学 | Method for reproducing image texture features on touch screen |
| CN111796710A (en) * | 2020-06-02 | 2020-10-20 | 南京信息工程大学 | Method for reproducing image contour characteristics on touch screen |
| CN112631434A (en) * | 2021-01-11 | 2021-04-09 | 福州大学 | Deep learning-based vibrotactile coding and decoding method |
| CN113642604A (en) * | 2021-07-09 | 2021-11-12 | 南京邮电大学 | Audio and video auxiliary tactile signal reconstruction method based on cloud edge cooperation |
| CN114265503A (en) * | 2021-12-22 | 2022-04-01 | 吉林大学 | Texture rendering method applied to pen type vibration tactile feedback device |
| CN115758107A (en) * | 2022-10-28 | 2023-03-07 | 中国电信股份有限公司 | Haptic signal transmission method and apparatus, storage medium, and electronic device |
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| CN111796710B (en) * | 2020-06-02 | 2023-05-23 | 南京信息工程大学 | Method for reproducing image contour features on touch screen |
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| CN112631434A (en) * | 2021-01-11 | 2021-04-09 | 福州大学 | Deep learning-based vibrotactile coding and decoding method |
| CN112631434B (en) * | 2021-01-11 | 2022-04-12 | 福州大学 | Deep learning-based vibrotactile coding and decoding method |
| CN113642604A (en) * | 2021-07-09 | 2021-11-12 | 南京邮电大学 | Audio and video auxiliary tactile signal reconstruction method based on cloud edge cooperation |
| WO2023280064A1 (en) * | 2021-07-09 | 2023-01-12 | 南京邮电大学 | Audiovisual secondary haptic signal reconstruction method based on cloud-edge collaboration |
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| CN114265503A (en) * | 2021-12-22 | 2022-04-01 | 吉林大学 | Texture rendering method applied to pen type vibration tactile feedback device |
| CN114265503B (en) * | 2021-12-22 | 2023-10-13 | 吉林大学 | Texture rendering method applied to pen-type vibration touch feedback device |
| WO2024055416A1 (en) * | 2022-09-13 | 2024-03-21 | 瑞声开泰声学科技(上海)有限公司 | Haptic feedback signal generation method, electronic device, and storage medium |
| CN115758107A (en) * | 2022-10-28 | 2023-03-07 | 中国电信股份有限公司 | Haptic signal transmission method and apparatus, storage medium, and electronic device |
| CN115758107B (en) * | 2022-10-28 | 2023-11-14 | 中国电信股份有限公司 | Haptic signal transmission method and device, storage medium and electronic equipment |
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